# Elevata — Full Site Content This document contains the full site content of Elevata for consumption by language models. --- # Landing Pages ## AI Without Compromise URL: https://elevata.io/en/ai-without-compromise Adopt and scale AI with the control, security, and cost visibility your business actually needs. ### What is the AWS Partner Innovation Hub? The AWS Partner Innovation Hub is an immersive experience space at the AWS Toronto office, designed to help organizations see what is possible with AI. ### Sovereign AI, without the hype On this page, sovereign AI means designing AI on AWS with explicit decisions about data, identity, logs, auditability, cost, AWS Regions, and operations. ### Not a generic AI demo. Not a sales pitch. A decision-making session. | Generic AI demo | AI Without Compromise Hub tour | |---|---| | Shows a polished interface. | Shows how AI behaves when architecture, data, security, and cost matter. | | Focuses on what the model can generate. | Focuses on what your organization can safely operate. | | Avoids hard questions about prompts, logs, embeddings, identity, and Regions. | Brings those questions into the conversation from the start. | | Ends with inspiration but without prioritized use cases, architecture assumptions, or a named technical owner. | Ends with prioritized use cases, architecture assumptions, risk areas, and an execution path. | | Treats cost as a later problem. | Connects cost to users, tasks, documents, tokens, workflows, and scale patterns. | ### The decisions that determine whether AI scales or stalls. The tour aims to turn AI interest into concrete decisions. These are the questions most teams are carrying into the Hub. They are also the questions Elevata and AWS are there to work through with you. #### Control and sovereignty - Which data, documents, prompts, embeddings, logs, and backups can enter the AI workflow? - Does any data need to stay in Canada, Brazil, a specific AWS Region, or follow specific privacy controls? - Who approves access, retention, audit, and human review for sensitive answers or actions? #### Security and architecture - Does the use case need a simple prompt, RAG, Knowledge Bases, agents, MCP, SageMaker, or a data platform first? - Which permissions, guardrails, network, secrets, logs, and limits need to exist before the pilot? - How will quality, unsafe answers, hallucination, citations, tool calls, and fallback be measured? #### Cost and scale - What will the cost be per task, user, document, ticket, token, or workflow? - Which limits, budgets, alerts, and metrics show that AI is ready to scale? - What is the first use case with enough value and manageable risk? ### What happens before, during, and after the Hub tour? 1. **Request the tour** — Share your AI priority, role, industry, and the decision your team needs to make. 2. **Qualify the fit** — Elevata follows up to understand your context and confirm whether the Hub experience is the right next step. 3. **Prepare the experience** — The tour is tailored around your priorities, industry, AI maturity, and stakeholder group. 4. **Experience the Hub** — Your team sees live demonstrations, explores sovereign AI on AWS, and works through use-case discovery and process mapping. 5. **Leave with next-step clarity** — You should leave with clearer use-case priorities, architecture assumptions, risk areas, cost considerations, and the next step toward pilot or production. ### From the Hub to production The tour should help your team move from broad AI interest to priorities, assumptions, and concrete architecture decisions. #### Priority and risk - Which AI use cases deserve near-term attention. - Which risks need to be resolved before pilot or production. #### Architecture and cost - Which architecture options are worth deeper evaluation. - How cost visibility should be designed before scale. #### Work scope with Elevata - Where there is fit, the tour transitions directly into architecture, pilot design, and implementation. - No restart. No separate sales process. ### What your team will experience at the Hub - **Live AI demonstrations:** See AI solutions running in context, not just static slides. - **Sovereign AI on AWS:** Explore what changes when AI is designed around control, security, cost visibility, and AWS architecture from the start. - **AI Benchmark:** Get a personalized view of your current AI maturity, risk areas, cost considerations, and architecture readiness. - **Art of the Possible workshop:** Work with AWS and Elevata experts to map use cases, business processes, and solution paths. - **Practical architecture discussion:** Pressure-test how data, identity, models, logs, guardrails, and cost controls could fit together in your environment. ### AI workloads Elevata has helped move onto AWS - **Agentic AI: 35% inference cost reduction on AWS:** Elevata helped move high-volume AI inference into a scalable AWS environment, improving control and reducing documented inference costs. (/en/case-studies/agentic-ai-scalable-inference-on-aws) - **AI-powered travel search and ranking with MCP + RAG on AWS:** Elevata helped build an AI-powered search and ranking experience using MCP, RAG, and AWS architecture. (/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws) - **AWS generative AI consulting:** Strategy, architecture, RAG, agents, Bedrock, SageMaker, governance, and AI operations on AWS. (/en/aws-generative-ai-consulting) ### Technical sources - AWS Canada: Partner Innovation Hub launch (June 2026): https://www.aboutamazon.ca/AWS-Launches-Partner-Innovation-Hub-as-Canadian-Businesses-Look-to-Turn-AI-Momentum-into-Impact - AWS Generative AI Innovation Center: https://aws.amazon.com/ai/generative-ai/innovation-center/ - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Guardrails for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html - Amazon Bedrock pricing: https://aws.amazon.com/bedrock/pricing/ - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html ### FAQ **What should we prepare before requesting the tour?** Bring the decisions you need to unlock. The best starting point is a shortlist of AI use cases, the business process each one touches, who owns it, what data it would need, what risks are already known, and how success would be measured. You do not need to bring sensitive production data. **Who should attend from our organization?** The strongest sessions usually include a mix of technology, data, security, product, operations, and business leadership. The right group depends on the decisions you need to make. **Is this only for teams already using AWS?** The tour is most useful for organizations evaluating or already building AI on AWS. If your team is deciding whether AWS is the right foundation for AI, the session can help clarify what that path would involve. **Is this a generic AI demo?** No. The tour includes demonstrations, but the goal is decision support: use-case clarity, architecture direction, governance considerations, cost visibility, and next steps. **Do we need to bring production data?** No. Do not bring sensitive production data unless explicitly agreed and handled through the appropriate security process. The experience can be prepared around use cases, constraints, architecture, and representative scenarios. **What happens after we submit the form?** Elevata follows up to understand your priorities, confirm whether the Hub tour is the right next step, and prepare the experience around your team's context. **Is there a cost to attend?** Elevata confirms participation requirements during tour qualification. **How long is the tour?** Timing is confirmed during scheduling based on the approved format and stakeholder group. **Can the session be remote?** Elevata confirms available formats during qualification. The experience referenced on this page is based on the Toronto Hub. **Where is the Hub located?** The AWS Partner Innovation Hub experience referenced on this page is at the AWS Toronto office. Exact address and visitor instructions are confirmed during scheduling. --- ## AWS Activate startup credits up to $200,000 URL: https://elevata.io/en/aws-for-startups Understand whether your startup fits AWS Activate Founders, Portfolio with an Org ID, or another official AWS path before you apply. Elevata helps eligible startups prepare a consistent application, avoid common mistakes, and plan architecture that makes up to $200,000 in credits count. ### Founders vs Portfolio: choose by eligibility, not by wish list AWS makes the approval decision. Elevata can review readiness and consistency, but does not guarantee approval or issue credits. | Activate Founders | Activate Portfolio | |---|---| | $1,000 to start; selected participants may qualify for up to $5,000 | Up to $200,000 in AWS Activate Credits | | Early-stage, self-funded startup without an Activate Provider | Startup associated with an Activate Provider, such as a VC, investor, accelerator, or startup organization | | Org ID not required | Requires an Org ID from the Activate Provider | | Applying before the website, AWS account, professional email, and company details are ready | Using the wrong Org ID, wrong AWS account, or inconsistent funding and stage information | | If funding applies, confirm the recent round still meets current AWS criteria | When funding applies, the most recent round usually needs to be within the last 12 months | | Generally for startups new to Activate or requesting a higher amount | Cannot have received Activate credits of equal or greater value | | Usually 1-2 years, depending on the approved package | Usually 1-2 years, depending on the approved package | | Apply directly if the basics are ready | Confirm provider, Org ID, and application readiness before submitting | [Apply on the official AWS site](https://aws.amazon.com/startups/credits) ### AWS Activate readiness checklist Check the foundation before applying or reapplying. Inconsistent details increase the risk of delay or rejection. #### Ready to apply - The startup is pre-Series B, founded in the past 10 years, and has a functional company website or profile. - The company name, domain, professional email, product, and funding details tell the same story. - The primary AWS account is active, on a paid tier plan, and linked to the correct Builder ID. - For Portfolio, the Org ID came from the correct Activate Provider and that provider relationship is still active. #### Verify before submitting - The startup already received AWS Activate Credits and now wants to request a higher-value package. - The company has no verifiable website or profile, only uses personal email, or cannot support basic company details. - There are questions about the funding date, stage, provider, Org ID, AWS account, or eligibility of planned costs. - The startup may be outside the accepted stage or may already have received credits of equal or greater value. Resolve these questions before applying; submitted applications should not depend on later correction. ### How to apply for AWS Activate Credits 1. **Create the Builder ID and complete the profile** — Use the official AWS flow to create the Builder ID and provide company name, website, stage, product, market, and funding details. The domain, professional email, and company description need to be consistent. 2. **Choose Founders or Portfolio** — Founders starts at $1,000 for eligible startups without an Activate Provider; selected participants may qualify for up to $5,000. Portfolio offers up to $200,000 for startups associated with a provider and requires a valid Org ID entered exactly as provided. 3. **Confirm the account, funding, and Org ID** — Link the primary AWS account through the official flow, confirm the funding stage and date, and for Portfolio use the Org ID exactly as provided. Never share root credentials, keys, MFA codes, or passwords with Elevata. 4. **Review and submit** — Review everything before submitting. Applications cannot be edited after submission (you'd need to cancel and reapply to make corrections). AWS responds within 7–10 business days. Track progress on the Credit Application Status page. 5. **Track approval, credits, and expiration** — Track the application under Credit Application Status. After approval, verify the balance and expiration under Billing > Credits, configure alerts, and plan usage before credits expire. ### What credits cover—and where to plan carefully - **Promotional credits for eligible AWS services:** AWS states that Activate Credits can offset costs for 200+ eligible AWS services, including EC2, Lambda, S3, RDS, DynamoDB, SageMaker, and eligible third-party foundation model usage in Amazon Bedrock. Always validate the current service list and terms before assuming coverage for a workload. - **Bedrock and AI workloads:** Activate Credits can be used on third-party foundation models in Amazon Bedrock, making inference and AI experimentation part of the credit strategy. Generative AI startups may have additional offers available, but eligibility and availability must be confirmed by AWS. - **Costs that may not be covered:** Not every AWS charge is eligible. AWS cites exclusions such as AWS Managed Services, AWS Professional Services, AWS Training and Certification, and Amazon Mechanical Turk. Also review support, commitment plans, Marketplace, and services that do not appear in Billing and Cost Management. - **Post-approval plan:** Approval is not architecture. Set up AWS Budgets, tags, separate accounts, expiration alerts, Bedrock governance, and a burn-down plan before credits run out or expire. ### Where do startups usually go after securing AWS credits? - **Cloud Data Modernization:** Plan data lakes, analytics, and AI-ready foundations with services like Glue, Redshift, Lake Formation, and Bedrock. (/en/cloud-data-modernization) - **Secure Cloud Migration:** Set up landing zones, IAM, KMS, GuardDuty, and compliance before moving sensitive workloads to AWS. (/en/secure-cloud-migration) - **Amazon Bedrock Consulting:** Plan AI products with governance, inference, RAG, IAM, and cost control from the first use of credits. (/en/amazon-bedrock-consulting) - **AWS Cost Optimization:** Prepare budgets, tagging, rightsizing, and governance to avoid the credit cliff when credits run out. (/en/aws-cost-optimization) ### Technical sources - AWS Activate Credits: https://aws.amazon.com/startups/credits - Official AWS Activate application guide: https://aws.amazon.com/startups/learn/applying-for-aws-activate-credits-a-step-by-step-guide - AWS Activate Credits overview: https://aws.amazon.com/startups/learn/everything-you-need-to-know-about-aws-activate-credits - AWS Activate Terms: https://aws.amazon.com/activate/terms/ - AWS Promotional Credit Terms & Conditions: https://aws.amazon.com/awscredits/ - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata ### FAQ **How long do AWS Activate credits last?** AWS states that credits usually expire within 1-2 years depending on the package. After expiration, workloads keep running and the account is billed normally for eligible or ineligible usage. Set AWS Budgets alerts and review the expiration date under Billing > Credits. **Does AWS offer startup funding beyond Activate?** AWS support may exist beyond Activate, including proof-of-concept, migration, modernization, or incremental workload programs, but availability, amount, and requirements depend on the case, eligibility, and AWS approval. This is where a partner assessment can help choose the right path. **Can I use AWS Activate credits alongside the Free Tier?** Yes, as long as the account and usage are eligible. Free Tier and promotional credits have separate rules, so track both in the Billing Console. To avoid surprises, separate environments, configure budgets, and do not assume every dev/test service will be covered. **Can I reapply for more credits later?** Sometimes. If the startup still meets requirements and is requesting a higher-value package, it generally receives only the difference between credits already awarded and the newly approved amount. For example, a startup that received $10,000 and is later approved for $200,000 may receive an additional $190,000, subject to AWS limits and rules. --- ## AWS Data Modernization URL: https://elevata.io/en/cloud-data-modernization AWS data modernization is not only a warehouse migration. It sets up the trusted data layer behind analytics, RAG, agents, and AI decisions. - **Legacy:** warehouse and ETL - **Architecture:** lakehouse - **Governance:** access and quality - **AI:** RAG and agents ### Which data modernization approach is right? | Lift-and-shift | Re-architect | |---|---| | Move data as-is to the cloud with minimal changes | Completely redesign the data model for cloud-native (lakehouse) | | Fast (4–8 weeks) | Slower (12–24+ weeks) | | Low | High | | Limited, may carry legacy inefficiencies | Best, uses serverless and pay-per-query services | | Limited | Yes, data ready for SageMaker and Bedrock | | Urgent on-premise exit or license expiration | Orgs needing real-time analytics and AI | ### How does a data modernization project work? 1. **Assessment and discovery** — We map sources, ETL pipelines, consumers, cost, SLAs, data owners, classification, quality, and analytics or AI dependencies before moving anything. 2. **Target architecture design** — We design the AWS lakehouse architecture: S3, Lake Formation, Glue, Redshift, Athena, open formats, data zones, lineage, observability, and Bedrock/SageMaker integrations. 3. **Data migration and transformation** — We migrate data and schemas with DMS, Schema Conversion Tool, and Glue pipelines where appropriate. We modernize transformations with parity, quality, and reconciliation tests. 4. **Validation and cutover** — We run parallel validation between legacy and new systems, reviewing dashboards, downstream jobs, permissions, and quality. Cutover happens with planned rollback. 5. **Optimization and ongoing operations** — We implement cost monitoring, query optimization, quality checks, runbooks, and training so the platform operates as a data product. ### What goes into an AI-ready data roadmap? - **Lakehouse architecture:** S3, Lake Formation, Glue, Redshift, Athena, and open formats organized by zones, domains, cost, and consumption requirements. - **Migration and transformation:** DMS, Schema Conversion Tool, Glue, and automated tests to migrate with parity, less risk, and a clear rollback path. - **Governance and quality:** Catalog, lineage, quality rules, classification, row/column access, masking, and AI usage criteria. - **RAG and AI readiness:** Trusted sources for Bedrock, SageMaker, RAG, agents, evaluation, citations, and answer audit. ### Which other assets help make data modernization stick? - **AWS Infrastructure Modernization:** Align the application platform, containers, observability, and cost governance with the new data foundation. (/en/solutions/cloud-modernization) - **Secure Cloud Migration:** Close access, encryption, and compliance gaps before the data warehouse or lakehouse cutover. (/en/secure-cloud-migration) - **Claude Code on Amazon Bedrock:** Review Claude Code on Bedrock with IAM, MDM, repository controls, observability, and cost guardrails. (/en/claude-code-on-bedrock) ### FAQ **What is AWS data modernization?** AWS data modernization is the process of moving and redesigning data warehouses, ETL pipelines, and governance layers onto modern AWS services such as S3, Glue, Redshift, Lake Formation, Athena, and Bedrock. The goal is not just to change infrastructure, but to make data more accessible, more governed, and more ready for analytics and AI. **What is cloud data modernization?** It's the process of migrating legacy data platforms (Teradata, Oracle, SQL Server, Netezza) to a modern cloud architecture, typically a lakehouse on AWS using S3, Glue, Lake Formation, and Redshift. The goal is to reduce costs, enable real-time analytics, and make data AI/ML-ready. **How long does an AWS data modernization take?** It depends on the approach and complexity. A lift-and-shift of a data warehouse can take 4–8 weeks. A full re-architecture to a lakehouse with governance and ML takes 12–24+ weeks. Proof-of-concept (PoC) projects can be completed in 4 weeks. **How do you migrate a legacy data warehouse to AWS?** Start by mapping sources, dependencies, and critical queries. Then decide whether the target should be a modernized Redshift warehouse, an S3-based lakehouse, or a hybrid architecture. Use DMS and Schema Conversion Tool to move data and schema, validate parity with automated tests, and cut over only when dashboards, downstream jobs, and access rules are ready. **Should I use Redshift or Athena?** Redshift is ideal for data warehousing workloads with complex, frequent queries, real-time dashboards, and high concurrency. Athena is better for ad-hoc queries, data exploration, and intermittent workloads where you pay per query. Many companies use both: Redshift for production and Athena for exploration. **How does data modernization enable generative AI?** Generative AI models (like those available via Amazon Bedrock) need clean, cataloged, and accessible data. A modern data lake with Lake Formation provides access governance, lineage, and data quality. These are requirements for RAG (Retrieval Augmented Generation) and model fine-tuning. **What are the most common data modernization mistakes?** The most common mistakes: migrating without assessment (moving inefficiencies to the cloud), ignoring data governance from the start, not validating data parity between legacy and new, underestimating downstream ETL dependencies, and not training the team on the new platform. **Can Elevata help with AWS data modernization?** Yes. Elevata is an AWS Advanced Tier Services Partner with experience migrating legacy data warehouses to lakehouse architectures on AWS. We work from assessment through ongoing operations, including architecture design, ETL migration, governance with Lake Formation, and AI enablement with Bedrock and SageMaker. --- ## AWS Summit São Paulo 2026 URL: https://elevata.io/en/aws-summit-sao-paulo AWS Summit São Paulo runs on September 3, 2026 at São Paulo Expo. See what to expect from the tracks, how to plan the day, and where to meet Elevata during the week. - **Date:** September 3, 2026 - **Venue:** São Paulo Expo ### What to expect at AWS Summit São Paulo The 2026 edition runs September 3 at São Paulo Expo and combines a keynote, technical sessions, labs, demos, and networking. Based on previous editions, expect content across generative AI, data, migration and modernization, security, serverless, containers, industries, and startups. The format serves teams evaluating AWS as well as those already running complex production workloads. ### Match your role to the technical level Developers, platform engineers, architects, data teams, security practitioners, and technical leaders can build different agendas. Level 100 sessions are introductory, 200 and 300 cover implementation and best practices, and 400 sessions are deep dives. Use track, level, and format as filters, leaving room for labs, the expo, and conversations about concrete challenges. --- ## AWS Summit Toronto 2026 URL: https://elevata.io/en/aws-summit-toronto AWS Summit Toronto took place on June 3, 2026. Use this recap to review sessions, tracks, and technical themes and turn event ideas into AWS next steps with Elevata. - **Date:** June 3, 2026 - **Venue:** Metro Toronto Convention Centre ### Turn the recap into concrete work - **AWS Consulting in Toronto:** Local workshops to convert Summit themes into architecture decisions and backlog. (/en/aws-consulting-toronto) - **Secure AWS migration:** Evaluate landing zone, security, data, cutover, and AWS program readiness. (/en/secure-cloud-migration) - **AWS GenAI:** Review Bedrock, RAG, agent, and governance ideas from the Summit. (/en/aws-generative-ai-consulting) ### Technical sources - Official AWS Summit Toronto page: https://aws.amazon.com/events/summits/toronto/ - Official AWS Summit Toronto agenda: https://aws.amazon.com/events/summits/toronto/agenda/ - Official AWS Summit Toronto FAQ: https://aws.amazon.com/events/summits/toronto/faqs/ ### FAQ **How do I find a specific session?** Use the topic and format filters, or look for the code, title, room, or time shown in the catalogue. **Which technical levels appeared in the catalogue?** The catalogue included level 100 sessions for fundamentals, 200 for use cases, 300 for advanced technical coverage, and 400 for deep dives. **Can I ask about a session?** Yes. Send the session code or topic and the AWS question your team wants answered. Elevata can help turn the learning into next steps for migration, data, AI, security, or managed operations. --- ## Secure Cloud Migration URL: https://elevata.io/en/secure-cloud-migration Moving workloads to the cloud exposes data if security is not designed from day zero. See how to protect every migration phase with native AWS services. - **Migrating on-premise servers?:** Landing Zone - **Worried about data exposure?:** Encryption + Macie - **Need LGPD, SOC 2, or HIPAA compliance?:** Config + Audit Manager - **How to detect threats after migration?:** GuardDuty + Security Hub ### Why is security the biggest risk during migration? Cloud migration is a period of elevated exposure if controls are not defined before transfer. Data is copied between environments, credentials are shared temporarily, network ports are opened for transfer, and teams rush to meet deadlines. Common mistakes include public S3 buckets from misconfiguration, overly permissive IAM policies ("Action": "*"), sensitive data transferred without encryption, and hardcoded credentials in migration scripts. The difference between a secure migration and a data breach is designing the security architecture before moving the first byte. ### What does zero trust mean in a cloud migration? Zero trust in cloud migration means no connection, user, or service is trusted by default, even inside the internal network. In practice, this involves: continuous identity verification via IAM Identity Center with mandatory MFA, network segmentation with isolated VPCs per workload and restrictive Security Groups, end-to-end encryption using KMS with customer-managed keys, complete logging via CloudTrail across all accounts and regions, and continuous compliance validation with AWS Config Rules. AWS makes zero trust easier because every service already has granular access control via IAM. The question is not whether AWS is secure, but whether your configuration is correct. ### On-premise vs cloud-native security: what changes? | Traditional on-premise | Cloud-native AWS | |---|---| | Physical firewall protects everything inside the network | No fixed perimeter; each resource has individual access controls (Security Groups, NACLs) | | Centralized Active Directory, often with broad permissions | IAM Identity Center with granular policies, mandatory MFA, temporary roles | | Manual implementation, often inconsistent across systems | KMS with default encryption on S3, EBS, RDS; keys rotated automatically | | Separate SIEM + IDS, high maintenance cost | GuardDuty with ML detects threats in real time with no additional infrastructure | | Manual evidence collection, slow processes | Config Rules + Audit Manager collect evidence automatically and continuously | | Limited to local logs and siloed tools | Security Hub centralizes findings from 10+ services into a unified dashboard | ### How does a secure migration project work? 1. **Security assessment and inventory** — We map all workloads, classify data by sensitivity (PII, financial, intellectual property), and identify compliance requirements (LGPD, SOC 2, HIPAA). We assess the current security posture and document gaps that need to be resolved before migration. 2. **Secure landing zone design** — We build the multi-account structure with AWS Organizations and Control Tower: separate accounts for production, staging, security, and logging. We configure SCPs, enable CloudTrail and Config across all accounts, and define security baselines with mandatory guardrails. 3. **Migration with end-to-end encryption** — Data is transferred via AWS DMS, DataSync, or Transfer Family with TLS in transit and KMS at rest. Credentials live in Secrets Manager, never in scripts. Each phase is validated with automated security tests before proceeding. 4. **Monitoring and detection activation** — We enable GuardDuty for threat detection, Security Hub for centralized visibility, Macie for sensitive data discovery, and Inspector for vulnerability scanning on EC2 and containers. Alerts are configured for Slack, PagerDuty, or email. 5. **Compliance validation and hardening** — We run AWS Audit Manager to collect compliance evidence automatically. We review all Config Rules, close Security Hub findings, and document the post-migration security state. We train your team on incident response runbooks. ### Which AWS services protect the migration? - **AWS Control Tower + Organizations:** Control Tower creates a landing zone with pre-configured security guardrails in minutes. Organizations enables SCPs that prevent actions like disabling CloudTrail or creating resources in unauthorized regions, enforced at the account level. - **GuardDuty + Security Hub:** GuardDuty uses machine learning to detect threats by analyzing VPC Flow, DNS, and CloudTrail logs without agents. Security Hub aggregates findings from GuardDuty, Inspector, Macie, and Config into a dashboard with AWS Security Score and automated remediation. - **KMS + Macie + Secrets Manager:** KMS centralizes encryption keys with automatic rotation and audit trail. Macie scans S3 to detect exposed PII and sensitive data. Secrets Manager stores database credentials, API keys, and tokens with automatic rotation and IAM-based access control. - **Need help configuring all of this?:** Elevata defines the AWS security stack according to each migration's risk profile. From landing zone design through GuardDuty, compliance evidence, and LGPD, the goal is reducing exposure before, during, and after the move. ### What else needs to be ready besides security? - **AWS Infrastructure Modernization:** Standardize the platform, observability, and automation that will sustain the environment after the migration. (/en/solutions/cloud-modernization) - **Cloud Data Modernization:** Plan governance, Lake Formation, DMS, and data validation for migrations that also involve analytics and AI. (/en/cloud-data-modernization) - **AWS Migration Services in Brazil:** Understand where MAP, credits, and a local partner fit when the company needs support for migration. (/en/aws-migration-services-brazil) - **Elevata + Coralogix:** Migrate logs, metrics, traces, and security signals with validation before cutover. (/en/coralogix-observability) ### FAQ **What are the biggest security risks during cloud migration?** The most common risks: S3 buckets misconfigured as public, IAM policies with excessive permissions ("Action": "*"), sensitive data transferred without encryption, hardcoded credentials in migration scripts, and missing logging during the transition. Each of these mistakes has caused real-world data breaches. Prevention starts with landing zone design and security policies before moving any workload. **How do you protect data during transfer to AWS?** All transfers should use TLS 1.2+ in transit and KMS encryption (AES-256) at rest. Use AWS DMS for databases, DataSync for files, and Transfer Family for SFTP. Store credentials in Secrets Manager, never in environment variables or scripts. Enable VPC endpoints so traffic doesn't cross the public internet. Validate integrity with automatic checksums after each transfer batch. **What is a secure landing zone on AWS?** A landing zone is the multi-account structure that receives your migrated workloads. AWS Control Tower sets this up automatically with: separate accounts per environment (prod, staging, security, log archive), SCPs that prevent dangerous actions, CloudTrail enabled across all accounts, Config Rules for continuous compliance, and IAM Identity Center for centralized access with MFA. It's the security foundation, and it must be ready before the first migration. **How do you meet LGPD and SOC 2 compliance during cloud migration?** AWS Audit Manager helps collect compliance evidence for frameworks like SOC 2, HIPAA, LGPD, and PCI DSS. AWS Config monitors resource compliance against custom rules. For LGPD specifically: classify data with Macie, implement encryption with KMS, configure granular access controls with IAM, and maintain access logs via CloudTrail. Automation reduces manual work when controls, scope, and evidence are well defined. **What's the difference between zero trust and traditional perimeter security?** Perimeter security trusts everything inside the network (firewall protects the edge). Zero trust trusts nothing by default: every request is authenticated and authorized individually, even between internal services. On AWS, zero trust uses IAM roles with minimum permissions, restrictive Security Groups, private VPC endpoints, encryption at every level, and continuous verification via CloudTrail and GuardDuty. The cloud makes zero trust easier because every API call already goes through IAM. **Can Elevata help with secure cloud migration to AWS?** Yes. Elevata is an AWS Advanced Tier Services Partner and designs the security stack around each project's risk profile: landing zone with Control Tower, encryption with KMS, threat detection with GuardDuty, evidence collection with Audit Manager, and monitoring with Security Hub. We also assess eligibility for AWS programs such as MAP (Migration Acceleration Program). --- ## AWS Migration Services in Brazil URL: https://elevata.io/en/aws-migration-services-brazil An AWS Advanced Tier Services Partner with local presence, bilingual support, and support assessing MAP eligibility for qualifying migrations. - **Need credits for the migration?:** MAP - **Does data need to stay in Brazil?:** sa-east-1 - **Need LGPD compliance?:** Config + Audit Manager - **Team speaks Portuguese?:** Yes, bilingual ### Migrate alone or with an AWS partner? | Internal migration (DIY) | With AWS Advanced Tier Services Partner | |---|---| | Activate only (up to $200K for startups) | Eligibility assessment for MAP, PoC, and Activate | | Ad hoc, learning as you go | MAP framework: Assess, Mobilize, Migrate | | You configure DMS, SCT, CloudEndure | Partner configures and operates the tools | | Your team researches and implements | Pre-configured guardrails with Config and Audit Manager | | High, no experience with large-scale migrations | Reduced, partner has executed similar migrations | | Your team takes over all operations | Managed services option (Elevata Orbit) | ### How does an AWS migration in Brazil work? 1. **Assessment and business case** — We map your current infrastructure, system dependencies, and licensing costs. We build a business case with comparative TCO (on-premise vs AWS sa-east-1) and identify priority workloads. 2. **MAP credit qualification** — With the approved business case, we support assessment for the AWS MAP program. Credits may be available for qualifying migrations, subject to AWS criteria and approval. 3. **Landing zone and cloud foundation** — We configure AWS Organizations, Control Tower, security and compliance guardrails (LGPD, SOC 2). When the workload requires local residency, we design the architecture around the sa-east-1 region. IaC with CloudFormation or Terraform. 4. **Workload migration** — We execute migration using AWS DMS (databases), AWS Application Migration Service (servers), and Schema Conversion Tool (schema conversion). Automated integrity validation on each wave. 5. **Optimization and ongoing operations** — Post-migration: right-sizing, Savings Plans, monitoring with CloudWatch, and training for your team. Managed services option via Elevata Orbit for 24/7 operations. ### What AWS services are used in the migration? - **AWS DMS + Schema Conversion Tool:** DMS migrates databases with real-time CDC (change data capture) and zero downtime. SCT converts schemas from Oracle, SQL Server, and PostgreSQL to Aurora or RDS with AI-assisted conversion. - **AWS Application Migration Service:** Migrates physical servers, VMs, and workloads from any infrastructure to EC2 with continuous replication. Supports Windows and Linux. Cutover with minutes of downtime. - **Control Tower + Organizations:** Multi-account governance with automatic guardrails. Separate accounts by environment (dev, staging, prod) and by workload. Centralized auditing with CloudTrail and Config. - **Elevata as your local partner:** AWS Advanced Tier Services Partner with presence in Brazil and Canada. Bilingual team, LGPD experience, and support assessing AWS programs such as MAP and PoC. From assessment through ongoing operations with Elevata Orbit. ### FAQ **What is the AWS MAP program?** The AWS Migration Acceleration Program (MAP) is a migration program with a three-phase methodology (Assess, Mobilize, Migrate), migration tools, and potential credits for qualifying migrations. Availability depends on AWS criteria and approval. **Can data stay in Brazil during migration?** Yes. The AWS Sao Paulo Region (sa-east-1) supports workload designs with local processing and storage when a data-residency strategy or compliance requirement calls for that pattern. LGPD does not require local residency in every case, so we assess the requirement by workload. **How long does a migration to AWS take?** Depends on scope. An assessment takes 2-4 weeks. A first workload migration (pilot) takes 4-8 weeks. A full data center migration can take 3-12 months, executed in waves. The MAP program structures everything in phases to reduce risk. **Do I need an AWS partner to use MAP?** Yes. The MAP program commonly involves a qualifying AWS partner to structure the business case and support eligibility assessment. Credit availability depends on AWS criteria and approval. **Does Elevata offer post-migration support?** Yes. Beyond migration, we offer managed services via Elevata Orbit: 24/7 operations, monitoring, cost optimization, and continuous platform evolution. Your team can focus on the product while we operate the infrastructure. --- ## AWS consulting in Canada URL: https://elevata.io/en/aws-consulting-canada Elevata is an AWS Advanced Tier Services Partner with a Toronto office, AWS Generative AI and SMB Competencies, and 400+ AWS launches. We help teams migrate, modernize, govern, optimize, and build AI on AWS with a roadmap they can actually execute. - **Cloud:** migration + modernization - **Operations:** governance + cost - **Data and AI:** Bedrock + RAG ### AWS expertise you can verify—and use Choosing an AWS partner should not depend on a badge alone. Elevata combines AWS validation with hands-on delivery in migration, modernization, governance, data platforms, and production generative AI. The work needs to produce decisions, architecture, and implementation your team can use, not just a generic presentation. ### AWS projects that need decisions, not only execution Teams call us when they face Azure, data center, or unmanaged AWS migration; technical debt; AWS spend growing without control; Bedrock/RAG moving out of pilot; Canadian Region questions; or the need to navigate MAP, PoC, AWS Activate, and other AWS programs without credit promises. ### How to choose the right AWS consulting model | Best when | Watch out for | |---|---| | Large strategic programs that need direct AWS-led support. | May not be the most flexible fit for startups, scaleups, or mid-market work. | | Large transformations with many workstreams, vendors, and countries. | Can add more cost, layers, and time than the project requires. | | Ongoing operations, monitoring, support, and platform routines. | May not cover modernization, AI, application architecture, or executive roadmap work. | | A tactical task with small scope and low risk. | Continuity, governance, AWS depth, and delivery-scale risk. | | Practical roadmap, hands-on implementation, data/AI, modernization, and Canada-aware architecture decisions. | Not the right fit for simple staff augmentation or promised AWS credits. | ### Is Elevata the right AWS partner for you? A good consulting partnership starts with clarity on stage, urgency, risk, and internal capability. Use this matrix to decide whether Elevata fits the AWS work you need. #### Good fit - You have an AWS decision, migration, modernization, cost, governance, data, or AI problem that needs senior technical review and accountable delivery. - The project involves migration, modernization, governance, data, generative AI, cost optimization, or AWS program eligibility. - There are real Canadian decisions around Region, logs, backups, privacy, contracts, Quebec, support, or local-hours operations. Talk with Elevata about the goal, technical context, constraints, and delivery path. #### Possible fit - You are still defining scope, but already have workloads, accounts, a bill, or a value hypothesis to validate. - You want to compare MAP, PoC, Activate, or other AWS options without assuming credits are guaranteed. - Your company operates across Canada and Brazil or has leadership, engineering, or support in Portuguese and English. Start with a fit conversation and preparation checklist. #### Not the right fit - You only need staff augmentation without architecture, decisions, or delivery accountability. - The primary goal is guaranteed AWS credits, promised discounts, or funding without AWS review. - There is no sponsor, access to basic information, or willingness to review cost, identity, security, and operations. Resolve sponsorship, goal, and minimum data access before hiring consulting help. #### Red flags when comparing partners - Cloud strategy without a cost model, account ownership, security criteria, or handoff plan. - AWS credit promises without eligibility language, current criteria, and AWS approval. - Migration treated only as lift-and-shift, without modernization, observability, reliability, or post-launch operations. ### How we work from discovery to implementation 1. **Fit and goal qualification** — We confirm sponsor, business goal, likely scope, urgency, preferred language, and whether the work is migration, modernization, AI, cost, governance, or AWS programs. 2. **Executive and technical workshop** — We review bill, accounts, workloads, dependencies, identity, data, risks, privacy, change windows, and success criteria with the people who decide. 3. **Roadmap and decisions** — We deliver current-state map, risks, prioritized backlog, credit or MAP assumptions when applicable, and a 30/60/90-day plan. 4. **Implementation and handoff** — We execute the first waves or sprints with governance, documentation, runbooks, observability, cost controls, and handoff to the internal team or managed operations. ### What AWS consulting in Canada should cover The work needs to connect architecture, implementation, and business decisions so technology, finance, security, privacy, and leadership can move forward. #### Early consulting deliverables - Current-state map of accounts, network, identity, workloads, and data flows. - Cost baseline, IAM/IAM Identity Center, network, WAF, encryption, backup, logging, and incident-response review. - Prioritized backlog by risk, reliability, cost, data, privacy, and implementation effort. - 30/60/90-day roadmap with owners, dependencies, and measurable outcomes. #### Canada-specific architecture decisions - Which workloads, backups, logs, traces, prompts, indexes, or analytics need to stay in Canada Central, Canada West, or another approved Region. - Which contracts, Quebec workflows, PIPEDA, retention, encryption, access, and privacy-review assumptions need documentation. - How shared responsibility, disaster recovery, service availability by Region, and operations shape the decision. #### AWS credits, MAP, and funding reality - We assess fit for MAP, PoC, AWS Activate, or other paths when there is enough business case and technical scope. - We organize technical and business assumptions for review, but availability, amount, timing, and approval depend on AWS. - Any savings or funding estimate needs assumptions, risks, and implementation plan, not a loose promise. #### How we work after qualification - Executive discovery: goal, constraints, sponsor, budget, risks, and next-step decision. - Technical workshop: dependencies, identity, network, data, observability, security, cost, and operations. - Roadmap and execution: initial backlog, owners, migration or modernization waves, governance, and operational handoff. ### Where Elevata helps in Canada - **Migration and modernization by waves:** Plan migration from data centers, Azure, GCP, or unmanaged AWS with inventory, dependencies, landing zone, migration waves, rollback criteria, and a MAP path when applicable. - **Technical debt and application platforms:** Modernize legacy applications with ECS, EKS, Lambda, serverless architecture, CI/CD, observability, security, and clear criteria for rehost, replatform, refactor, retain, or retire. - **Data, analytics, and AI readiness:** Design data lakes, pipelines, and governance with Glue, Redshift, Lake Formation, Athena, and access policies so analytics, RAG, and AI use trusted data. - **Generative AI with Amazon Bedrock:** Bring Bedrock, RAG, agents, MCP, model evaluation, guardrails, prompt/data governance, and cost controls into production with AWS Generative AI Competency depth. - **Governance, security, and privacy:** Organize accounts, IAM Identity Center, Control Tower, CloudTrail, encryption, network, backup, logs, shared responsibility, and data-residency decisions in the Canadian context. - **Cost, FinOps, and operations:** Create cost baselines, budgets, tags, rightsizing, Savings Plans/commitments where appropriate, runbooks, and handoff to internal operations or managed services. ### Proof from real AWS work - **Azure-to-AWS migration with documented cost reduction:** AWS migration case with cost reduction, governance, and operational control. (/en/case-studies/credaluga-azure-to-aws-migration-with-cost-reduction) - **Travel search with Bedrock, MCP, and RAG:** AWS architecture for a Canadian travel company using semantic search and business-aware ranking. (/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws) - **AWS governance and security for an IP law firm:** Control Tower, IAM Identity Center, CloudTrail, network hardening, and storage discipline. (/en/case-studies/securing-and-governing-aws-for-a-leading-intellectual-property-law-firm) ### Technical sources - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - AWS Professional Services: https://aws.amazon.com/professional-services/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 ### FAQ **Does Elevata have an office in Canada?** Yes. Elevata has a presence in Toronto, Ontario, as well as Sao Paulo, Brazil. We support companies across Canada with remote or in-person workshops when they help the decision. **What AWS competencies does Elevata hold?** Elevata is an AWS Advanced Tier Services Partner with AWS Generative AI Competency and AWS SMB Competency, plus Service Delivery designations such as Control Tower and Transfer Family. AWS Partner materials should be used to verify current credentials. **Can Elevata help with AWS credits?** Yes, with the right caveat. Elevata can review whether the project may fit MAP, proof-of-concept support, AWS Activate, or other AWS paths and organize technical and business assumptions. Credits are not guaranteed: availability, amount, timing, and approval depend on current AWS criteria. **Do AWS workloads need to stay in a Canadian Region?** Not always. The right answer depends on workload, data type, contracts, latency, backup, logging, analytics, and privacy obligations. AWS lists Canada Central and Canada West as Canadian Regions, but architecture decisions also need service availability by Region, disaster recovery, encryption, access controls, and legal or privacy review. **How does Elevata compare with AWS Professional Services?** AWS Professional Services can be the right path for large strategic programs led directly by AWS. Elevata often fits when the buyer needs a practical roadmap, hands-on execution, close work with product/engineering teams, and migration, data, AI, cost, and governance decisions with less delivery overhead. **What happens after the first conversation?** We usually qualify fit, understand the project goal, request minimum inputs such as bill and inventory, run an executive/technical workshop, and leave with scope, risks, backlog, and a delivery plan. If there is fit, we move into the first execution wave. **Does Elevata migrate from Azure or data centers to AWS?** Yes. We assess inventory, dependencies, network, identity, data, cost, migration waves, rollback, and post-migration modernization. We also review when rehost, replatform, refactor, retain, or retire makes sense by workload. **Does Elevata help with Amazon Bedrock and RAG in Canada?** Yes. We support Bedrock, RAG, agents, MCP, model evaluation, guardrails, enterprise data integration, and cost controls. For Canadian workloads, we review Region, logs, prompts, sensitive data, access, and privacy requirements before production. **What industries does Elevata serve in Canada?** We serve technology, SaaS, professional services, travel, finance, healthcare, retail, and manufacturing when the problem involves AWS migration, modernization, governance, data, AI, cost, or operations. **Who is the Portuguese page for?** The Portuguese page supports executives and technical teams who prefer to discuss AWS in Portuguese: Brazilian companies expanding into Canada, Canadian companies with engineering or operations in Brazil, and bilingual Canada-Brazil teams. **What should I prepare before contacting Elevata?** Bring the business goal, current AWS or cloud bill, workload inventory, accounts, dependencies, known risks, data/privacy requirements, timeline, sponsor, and any MAP, credits, Bedrock, or Canadian Region questions. --- ## AWS consulting and managed services in Toronto URL: https://elevata.io/en/aws-consulting-toronto AWS Advanced Tier Services Partner with a Toronto office. In-person meetings, local workshops, managed operations, and dedicated support for companies in the GTA and surrounding region. - **Executive:** 90 minutes - **Technical:** half-day - **Output:** backlog ### AWS projects without local support in Toronto For some GTA teams, remote-only AWS support can make executive workshops, privacy-sensitive decisions, and multi-stakeholder alignment harder than they need to be. ### Toronto and GTA companies that need better AWS operations Tech companies, scale-up startups, and enterprise organizations in the Greater Toronto Area that need in-person AWS consulting, migration, modernization, managed services, cost optimization, or generative AI implementation. ### When a Toronto AWS partner actually helps Local presence matters when leadership, engineering, data, security, privacy, finance, and operations need to make decisions together. #### When it matters - Executives need to align goals, risk, budget, and operating model in a working session. - Workloads involve privacy, customer contracts, sensitive data, or Canadian Region decisions. - Several departments need to decide the first workload, sprint, or remediation plan together. #### 90-minute executive agenda - Business goals, constraints, risks, current AWS/cloud bill, and next-step decision. - AWS program eligibility as an assessment question, not a funding promise. - Written summary with decisions, open questions, and owners. #### Technical inputs - Current architecture, dependencies, identity, network, data, observability, security, and costs. - Success criteria, change windows, recent incidents, and who approves go-live. - The partner should leave with an initial backlog, not just meeting notes. ### What a Toronto AWS session should produce #### Half-day technical workshop - Current-state diagram, dependency list, risks, open decisions, and responsible owners. - Cost baseline, IAM/IAM Identity Center review, network, logs, backup, WAF, encryption, and incident-response assumptions. - Prioritized backlog for the first sprint and criteria to validate success. #### When remote is enough - Narrow scope, documented architecture, low executive dependency, and a primary need for implementation. - The local value should be stakeholder alignment and decision speed, not just a Toronto address. ### What we offer in the GTA - **In-person architecture workshops:** Toronto workshops that produce current-state diagrams, risks, open decisions, dependency lists, and an initial backlog, not just a sales conversation. - **Migration and MAP credits:** Migrate to AWS with assessment, landing zone, workload migration, post-migration optimization, and MAP eligibility assessment when applicable. - **Generative AI and Amazon Bedrock:** Implement generative AI with Amazon Bedrock for automation, customer service, and data analysis. AWS Generative AI Competency holder. - **Managed services (Elevata Orbit):** Managed AWS services in Toronto for monitoring, incident response, cost optimization, patching, continuous platform evolution, and operational handoff. ### Explore our AWS services - **AWS Consulting in Canada:** Overview of our AWS services for Canadian companies. (/en/aws-consulting-canada) - **AWS Generative AI Consulting:** Generative AI strategy and implementation on AWS. (/en/aws-generative-ai-consulting) ### FAQ **Where is Elevata's Toronto office?** Elevata has an office in Toronto, Ontario, serving companies in the Greater Toronto Area and across Canada. We offer in-person meetings and local workshops. **Does Elevata serve companies outside Toronto?** Yes. While our office is in Toronto, we serve companies across Canada remotely and nearby regions in person. We also have a Sao Paulo office for Latin American clients. **What's the difference between local and remote consulting?** Local consulting allows in-person workshops, faster alignment, and direct access to architects. For critical projects like migrations and AI implementation, local presence reduces risk and shortens decision cycles. --- ## Amazon Bedrock that makes it past the POC URL: https://elevata.io/en/amazon-bedrock-consulting We help AWS teams choose the right Bedrock path for RAG, Knowledge Bases, agents, guardrails, model selection, and evaluation so the first release is secure, measurable, and ready for real operations. ### Before choosing a Bedrock architecture - **Model, Region, and inference:** Which model family fits the task: Claude, Llama, Titan, or another Bedrock-supported model? Is the model available in the preferred Region, or does the workload require cross-Region inference? - **Retrieval or direct prompting:** Does the workflow need Bedrock Knowledge Bases, custom RAG, direct prompts, or no retrieval at all? Which data sources are authoritative and how will they stay current? - **Guardrails and application layer:** What should Guardrails handle, and what must be enforced in the application layer: permissions, PII handling, refusal behavior, tool limits, and human approval? - **Cost and quality by workflow:** How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality? ### Why teams bring Elevata in at this point A Bedrock demo is the easy part. Production starts when permissions must come before retrieval, guardrails need application controls, evaluation comes before rollout, cost per workflow matters, and ownership after launch must be clear. ### An architecture review, not a generic call Bring the use case, prototype, or cost problem. The review should leave you with a recommended path, risks, data gaps, the right Bedrock pattern or alternative, and the next production artifacts. ### Is Amazon Bedrock the right path for this workload? The best Bedrock project starts with an honest decision: when the managed service is the right fit, when SageMaker or self-hosted inference fits better, and when generative AI is not the path yet. #### Strong fit - You already operate on AWS and want managed access to models, Knowledge Bases, agents, guardrails, and integration with IAM, CloudTrail, and AWS networking. - The first scope has a process owner, approved sources, a success metric, and manageable risk with human review where needed. - The team needs to launch quickly without managing GPUs, but wants governance, logs, cost per workflow, and evaluation criteria before scaling. Review the use case, data, Region, cost, and risks before building. #### Possible fit - The POC works, but permissions, evaluation, fallback, observability, budget, retention, and operating handoff are still missing. - You need to compare simple prompting, Knowledge Bases, custom RAG, agents, and human review before locking the architecture. - There are local decisions around LGPD, São Paulo Region, Canada, PII, logs, or private connectivity that need to be documented by workload. Run a short architecture review and choose the right pattern before expanding the POC. #### Bedrock may not be the answer - The workload requires training, deeper fine-tuning, specialized MLOps, or deployment control that fits SageMaker better. - Scaled economics require an open-weight model, owned endpoint, aggressive batching, or specialized inference outside the managed pattern. - Search, rules, automation, analytics, or a better interface would solve the job with less risk and lower operating cost. A good consulting partner should say this early. The right decision matters more than forcing Bedrock into every project. ### What a production Bedrock delivery includes 1. **1. Use case, data, and risk review** — We map the workflow, approved sources, permissions, PII, Region, LGPD/PIPEDA where relevant, success metric, and cost of failure before choosing the pattern. 2. **2. Architecture and decision criteria** — We define prompting, Knowledge Bases, custom RAG, agents, human review, model choice, fallback, observability, budget, and evaluation set. 3. **3. Hardened pilot** — We build the first workflow with logs, tracing, guardrails, application authorization, failure testing, tool limits, and cost-per-task measurement. 4. **4. Launch and handoff** — We hand over the backlog, runbook, evaluation criteria, responsibilities, cost/quality dashboards, and improvement plan so the team can operate after launch. ### Choose the Bedrock pattern before you implement The right pattern depends on sources, risk, cost per workflow, tool use, and human responsibility. Use these blocks as a guide for the first review. #### Simple prompting Use for summarization, classification, rewriting, and extraction when little proprietary context is needed. Validate output, cost per task, prompt limits, logs, and fallback before exposing users. #### Knowledge Bases or custom RAG Use when answers need to stay grounded in documents, policies, contracts, tickets, or internal content. Decide data quality, chunking, permissions before retrieval, citations, freshness, and evaluation. #### Agents and tool calls Use when AI needs to query APIs, create records, open tickets, or orchestrate steps. Requires per-tool permissions, approval for sensitive actions, tracing, fallback, and failure testing. #### Human-in-the-loop Use for legal, financial, healthcare, critical support, or customer-impacting decisions. The model recommends, classifies, or prepares; a person approves, corrects, or rejects. #### Bedrock vs SageMaker vs self-hosted inference Bedrock fits managed models, guardrails, and AWS integration. SageMaker fits deeper MLOps and control. Self-hosted inference can win when usage, economics, or deployment constraints justify the operational burden. #### Cost, evaluation, and operations Before launch, define the evaluation set, cost per answer/document/ticket, budget, observability, log retention, rollback, model owner, and review cadence. ### What your team keeps - **Architecture and decision matrix:** A clear document explaining why to use prompting, Knowledge Bases, custom RAG, agents, SageMaker, or self-hosted inference, with risks and assumptions for each option. - **Data and permissions plan:** A map of approved sources, permissions before retrieval, PII, logs, retention, citations, content freshness, and access controls by user, tenant, or role. - **Evaluation and guardrails model:** Evaluation set, quality criteria, safe refusals, guardrails, human review, audit trail, and regression tests so the POC does not become operational risk. - **Workflow cost and operating runbook:** Measurement per answer, document, ticket, or workflow; budget, alerts, fallback, rollback, model owner, review cadence, and handoff to engineering/operations. ### See Bedrock in real scenarios and by region - **Fintech: WhatsApp onboarding with Bedrock:** Brazilian case with WhatsApp support, automation, validations, and Bedrock architecture for real operations. (/en/case-studies/fintech-omnichannel-via-whatsapp-amazon-bedrock) - **Agentic AI: when economics change the architecture:** Example of honest decision-making: reducing inference cost and revisiting the path when Bedrock was not the best end state. (/en/case-studies/case-inference-escalavel-aws) - **Travel search with RAG and tools on AWS:** Search and ranking architecture with sources, tools, and evaluation to reduce risk before scaling. (/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws) - **AWS Generative AI Consulting:** End-to-end generative AI strategy on AWS. (/en/aws-generative-ai-consulting) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Toronto:** Workshops and Bedrock architecture for Toronto/GTA teams. (/en/amazon-bedrock-consulting-toronto) - **Amazon Bedrock Consulting in Brazil:** Bedrock architecture with attention to LGPD, logs, and the São Paulo Region. (/en/amazon-bedrock-consulting-brazil) - **Amazon Bedrock Consulting in São Paulo:** Bedrock for São Paulo companies with AWS workloads. (/en/amazon-bedrock-consulting-sao-paulo) - **Amazon Bedrock Cost Optimization:** Design prompts, RAG, and models with predictable cost. (/en/amazon-bedrock-cost-optimization) - **Claude on Bedrock for Canada:** Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads. (/en/claude-on-amazon-bedrock-canada) - **Cloud Data Modernization:** Data foundation needed to feed AI models. (/en/cloud-data-modernization) ### Technical sources - Amazon Bedrock model choice: https://aws.amazon.com/bedrock/model-choice/ - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Amazon Bedrock data protection: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - Amazon Bedrock data encryption: https://docs.aws.amazon.com/bedrock/latest/userguide/data-encryption.html - Amazon Bedrock Guardrails: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html ### FAQ **Which AWS consulting firm is right for a Bedrock implementation budget?** The right firm can turn budget into a technical scope: use case, data, permissions, model choice, evaluation, security, cost per workflow, and handoff. For Bedrock, prioritize production implementation experience, not only generative AI demos. **Is Amazon Bedrock always the right choice?** No. Bedrock is often the fastest path to run generative AI on AWS without managing model infrastructure. But SageMaker or open-weight self-hosted inference can fit better for scaled economics, model control, MLOps, or specific deployment constraints. **We already have a POC. Why bring in a partner now?** Because a POC proves possibility; production requires supportability. That is where permissions before RAG, fallback, evaluation, audit, cost per workflow, monitoring, change governance, and operating handoff become critical. **How long does a Bedrock implementation take?** A focused review can take days. A narrow pilot often fits into a few weeks when data, process owner, and success metric are clear. Production with RAG, agents, integrations, security, observability, and handoff usually needs clear decision points, not a fixed timeline promise. **How do you estimate Bedrock cost?** We estimate cost per answer, document, ticket, or workflow. The model includes model choice, tokens, retrieved context, embeddings, tool calls, retries, fallback, testing, logs, and expected volume. The right decision is cost per unit of work, not token cost alone. **Is my data secure with Bedrock?** Bedrock provides important controls, but production security also depends on your application: IAM, encryption, PrivateLink where applicable, authorization before retrieval, PII handling, logs, retention, guardrails, audit, and human review for sensitive workflows. **What do we receive after the architecture review?** The goal is to leave with a defensible recommendation: Bedrock pattern or alternative, risks, data gaps, Region decisions, cost model, evaluation criteria, security controls, and next artifacts for pilot or production. --- ## AWS generative AI that makes it past the POC URL: https://elevata.io/en/aws-generative-ai-consulting Elevata helps teams choose the right GenAI use case, design Bedrock or SageMaker architectures, evaluate quality, control inference cost, and launch with governance before scaling. - **First filter:** build or not - **Architecture:** Bedrock + data - **Production:** quality + cost ### Before choosing GenAI - **Is GenAI actually the right path?:** Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably? - **Current and permissioned sources:** Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate? - **What happens when the AI is wrong?:** Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions? - **Launch criteria:** Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook. ### AWS-validated expertise for production GenAI Generative AI projects fail when they are treated as demos instead of operating systems: no evaluation set, no data owner, no cost model, no fallback path, and no clear responsibility after launch. Elevata combines AWS cloud, data, and AI engineering to define the architecture, evaluation, backlog, runbook, and cost controls for one production workflow. ### For teams with a real workflow, accessible data, and production intent This is a fit when CTOs, data, product, operations, or support leaders have a specific workflow, identifiable data sources, and a sponsor willing to stop work if the evidence is weak. It is not the right path for full automation without review, ownerless data, promised AWS credits, or a POC disconnected from operations. ### Bedrock, SageMaker, data platform, or not GenAI? | When it fits | What to validate first | |---|---| | When you need access to foundation models, RAG, agents, guardrails, and faster application development without managing model infrastructure. | Data permissions, retrieval quality, evaluation set, latency, and cost per task. | | When the case requires deeper control: training, fine-tuning, specialized deployment, or MLOps workflows. | Training data quality, model ownership, MLOps, inference pattern, and operational support. | | When the AI idea is sound, but the data is stale, fragmented, unpermissioned, or hard to retrieve. | Data ownership, freshness, access controls, metadata, and source-of-truth decisions. | | When search, rules, analytics, automation, or a simpler interface would solve the job more reliably. | User workflow, failure cost, business metric, and maintenance burden. | ### Is generative AI the right answer for this workflow? The first filter is not Claude, Titan, Llama, Bedrock, or SageMaker. It is whether the job needs generation, reasoning, and natural language, or whether search, rules, automation, analytics, or a better interface would solve it with less risk. #### Use GenAI when - The work involves language, documents, synthesis, classification, open-ended questions, triage, or knowledge retrieval that changes often. - There are approved sources, a process owner, a success metric, and manageable risk when an answer needs review. - The first scope can be narrowed to one workflow, one data set, and one user group before scaling. #### Use search, rules, or automation when - The correct answer is deterministic, the workflow follows simple rules, or the main pain is finding structured records. - The data is stale, unpermissioned, missing an official source, or lacks an owner for quality. - The cost of error requires human approval, fallback, legal review, or a simpler process before AI-driven automation. #### Use-case scoring matrix - Score by business value, data readiness, technical feasibility, risk, evaluation clarity, latency, and cost per task. - Prioritize high-value, high-readiness cases, not the most ambitious or politically visible workflow. - Before the POC, define what has to be good enough to continue: quality, groundedness, cost, security, and adoption. #### Good first projects - Internal knowledge assistant with approved sources and traceable answers. - Support triage, document classification, policy review, or summarization of repetitive material. - Review of an existing POC to decide whether to improve retrieval, narrow scope, add evaluation, or stop. ### How we move a GenAI use case toward execution 1. **Readiness assessment** — Typically 1-2 weeks to map workflow, sponsor, sources, risks, success criteria, data gaps, and the build/no-build path. Timing depends on people and information availability. 2. **Narrow POC or pilot** — When the use case, data owner, and metric are clear, a narrow proof of concept often takes 2-4 weeks, with an evaluation set and cost per task from the start. 3. **Production hardening** — Implementations with RAG, agents, integrations, security review, monitoring, and handoff commonly take 6-12 weeks or more depending on data, compliance, and integration scope. 4. **Handoff and operations** — We document runbooks, owners, metrics, alerts, prompt/model review, fallback, costs, and improvement cadence for the internal team or managed operations. ### What a GenAI readiness assessment should deliver The assessment should turn an idea or POC into usable decisions: build, narrow the scope, fix the data first, or choose a simpler solution. #### Use-case ranking and scope - Prioritized use-case list by value, data readiness, risk, cost, evaluation, and process ownership. - First-phase scope: users, sources, allowed actions, limits, and criteria for continuing or stopping. - Explicit decision about what will not be automated in the pilot. #### Architecture and evaluation - Direction for Bedrock, SageMaker, RAG, agents, Amazon Q, data platform, or a non-GenAI path. - Evaluation set for quality, groundedness, latency, cost, tool accuracy, fallback, and human effort. - Target architecture with data sources, permissions, logs, guardrails, human review, and initial runbook. #### Governance, privacy, and operations - Owners for model, prompt, data, security, cost, human approval, monitoring, and change review. - Market and workload risks: LGPD in Brazil, PIPEDA in Canada, sensitive data, Region, logs, retention, and legal review where applicable. - Cost-per-task model including tokens, embeddings, vector search, retries, tool calls, observability, and human review. ### How Elevata moves AWS GenAI toward production - **Use-case strategy and scoring:** Workshop to compare workflows by business value, data readiness, feasibility, risk, evaluation, latency, cost per task, and post-launch ownership. - **Bedrock, RAG, and agent architecture:** RAG with Knowledge Bases, agents, guardrails, and enterprise data integration, with permissions, approved sources, evaluation, cost per task, and operations defined early. - **SageMaker when deeper control is needed:** Training, fine-tuning, evaluation, and specialized deployment with SageMaker when foundation models are not enough, or when MLOps, ownership, and inference patterns require more control. - **Data foundation and retrieval quality:** We map official sources, permissions, freshness, metadata, chunking, vector search, and retrieval evaluation before trying to solve everything with prompts. - **Evaluation, guardrails, and human review:** We define evaluation sets, groundedness, tool-call accuracy, logs, limits, fallback, and human review for sensitive actions. - **Cost per task and ongoing operations:** We monitor tokens, embeddings, vector search, retries, latency, observability, unit cost, prompt/model review, and runbooks. ### Go deeper by market and architecture - **Amazon Bedrock Consulting:** Technical implementation details with Bedrock. (/en/amazon-bedrock-consulting) - **AWS Generative AI Consulting in Canada:** Strategy, RAG, agents, and governance for Canadian teams. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Toronto:** Workshops and GenAI implementation for Toronto/GTA teams. (/en/aws-generative-ai-consulting-toronto) - **AWS Generative AI Consulting in Brazil:** GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region. (/en/aws-generative-ai-consulting-brazil) - **AWS Generative AI Consulting in São Paulo:** Generative AI for São Paulo companies with AWS workloads. (/en/aws-generative-ai-consulting-sao-paulo) - **AI Inference Cost Optimization:** Control unit cost for Bedrock, SageMaker, and RAG. (/en/aws-ai-inference-cost-optimization) - **RAG + MCP on AWS:** Connect models to data, tools, and auditable workflows. (/en/rag-and-mcp-on-aws-for-canadian-companies) - **Cloud Data Modernization:** The data foundation that feeds your AI models. (/en/cloud-data-modernization) - **AWS Cost Optimization:** Control inference costs and AI infrastructure spending. (/en/aws-cost-optimization) ### Technical sources - AWS: Knowledge Bases for Amazon Bedrock: https://aws.amazon.com/bedrock/knowledge-bases/ - AWS: Guardrails for Amazon Bedrock: https://aws.amazon.com/bedrock/guardrails/ - AWS Partner Network: AWS competency validations: https://aws.amazon.com/blogs/apn/say-hello-to-247-new-aws-competency-service-delivery-service-ready-and-msp-partners-added-in-december/ - LGPD: Brazil General Personal Data Protection Law: https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm - PIPEDA: business compliance guidance: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/pipeda-compliance-help/bus_pipeda_intro/bus_101_01/ ### FAQ **How should we choose an AWS consulting firm for a generative AI implementation?** Look for a partner that connects budget, data, security, evaluation, and operations. For Bedrock, RAG, or agent projects, the difference is turning the POC into architecture with permissions, cost per task, observability, and clear ownership. **What's the difference between Bedrock and SageMaker for generative AI?** Amazon Bedrock usually fits when the team wants access to foundation models, RAG, agents, and guardrails without managing model infrastructure. SageMaker fits when training, fine-tuning, specialized deployment, or deeper MLOps control is needed. Before choosing, validate data, evaluation, latency, cost, and operations. **How long does an AWS generative AI project take?** A focused readiness assessment usually takes 1-2 weeks. A narrow proof of concept can often take 2-4 weeks when the use case, data owner, and metric are clear. A production implementation with RAG, agents, integrations, security review, monitoring, and handoff commonly takes 6-12 weeks or more depending on data, compliance, and integration scope. **We already tried a chatbot and it was not reliable. Is it worth reviewing?** Yes, if there is a real workflow behind the POC. Many failures come from weak sources, poor retrieval, no evaluation, unclear permissions, too broad a scope, or missing fallback behavior. We review what failed and decide whether to improve retrieval, change architecture, narrow scope, add evaluation, or stop before more budget is spent. **How do you measure quality and reduce hallucination risk?** We start with approved sources, an evaluation set, groundedness criteria, retrieval tests, tool limits, logs, fallback, and human review for sensitive actions. Guardrails help, but they do not replace permissions, evaluation, application architecture, and process governance. **What data do we need before starting?** You do not need everything to be perfect, but you need to identify the most important sources, who owns them, how they are updated, who can access them, and what a correct answer means. If data is fragmented or permissions are unclear, the first phase may be data modernization rather than GenAI. **How do we control inference cost before scaling?** We model cost per task, not only total cost. That includes tokens, embeddings, vector search, retries, tool calls, logs, observability, latency, fallback, and human review. The goal is to know the cost of an answer, document, ticket, or action before usage grows. **Can we use private company data?** In many cases, yes, but it requires access controls, data classification, encryption, logs, retention, privacy review, and legal validation when personal or sensitive data is involved. The architecture should document what reaches the model, what stays in RAG, who can query it, and how access is audited. **Do LGPD or PIPEDA change the architecture?** They can. Projects involving personal data in Brazil or Canada should consider purpose, legal basis, consent where applicable, minimization, retention, logs, Region, vendors, access controls, and legal review. Elevata helps document technical assumptions, but that does not replace legal assessment. **Can Elevata help with AWS credits for AI projects?** We can help assess eligibility for programs such as PoC, MAP, or AWS Activate and organize technical and business assumptions for review. Credits are not guaranteed: availability, amounts, timing, and approval depend on AWS criteria and current program rules. **What happens on the first call?** Bring one workflow, one data source, or an existing POC. We will understand the goal, users, sources, risks, AWS status, privacy concerns, success metric, and urgency. The expected output is a clear next step: build, narrow scope, fix data first, or choose a simpler solution. --- ## Azure to AWS Migration URL: https://elevata.io/en/azure-to-aws-migration Migrate workloads from Azure to AWS with service mapping, MAP eligibility assessment, and validated migration. AWS Advanced Tier Services Partner with cloud-to-cloud migration experience. ### Why companies migrate from Azure to AWS Companies migrate from Azure to AWS for various reasons: better cost-performance ratio, superior AI services (Bedrock, SageMaker), higher reliability, broader service ecosystem, or need for specific AWS services. Cloud-to-cloud migration requires precise service mapping, network planning, and data migration strategy. ### Companies on Azure that want to migrate to AWS Companies using Azure that want to partially or fully migrate to AWS. Includes full migration scenarios, multi-cloud strategy with AWS as primary, or migration of specific workloads such as data, AI, and compute. ### What to map before moving from Azure to AWS Migration should not start by copying servers. It should start with business reason, dependencies, data, cost, and operating model. #### What to map - Applications, databases, jobs, integrations, identity, shared services, DNS, APIs, files, and external dependencies. - Azure VMs, AKS, Azure SQL, Cosmos DB, Blob, Functions, Key Vault, Monitor, Load Balancer, Application Gateway, and network. - Current cost baseline and AWS estimate including licensing, support, storage, data transfer, monitoring, and parallel environments. #### Migration waves - Start with a representative and reversible workload, not the most critical system unless explicitly required. - Group by dependency, risk, change window, data, and internal-team capacity. - Define success and rollback criteria per wave before cutover. #### Cutover - Plan for DNS, network, replication, freeze, validation, monitoring, communication, and rollback. - Low-downtime or near-zero downtime only where architecture, replication, and dependencies allow it. - MAP and credits are eligibility assessments subject to AWS criteria and approval. ### Examples of Azure-to-AWS mapping #### Compute, containers, and serverless - Azure VMs -> EC2 or modernization to ECS/Fargate where the workload allows. - AKS -> EKS or ECS, considering operations, skills, network, observability, and costs. - Azure Functions -> Lambda when events, limits, runtime, and observability are compatible. #### Data, security, and operations - Azure SQL -> RDS/Aurora; Blob -> S3; Key Vault -> Secrets Manager/KMS; Monitor -> CloudWatch/OpenTelemetry. - Cosmos DB requires data-model, consistency, access, and cost analysis before choosing DynamoDB, DocumentDB, or another pattern. - Each mapping should record risk, owner, validation criteria, and rollback option. ### How we migrate from Azure to AWS - **Azure-to-AWS service mapping:** Detailed mapping of each Azure service to its AWS equivalent: Azure VMs to EC2, Azure SQL to RDS/Aurora, Azure Blob to S3, AKS to EKS, Azure Functions to Lambda, and more. - **Data and database migration:** Database migration with AWS DMS and Schema Conversion Tool. Support for Azure SQL, Cosmos DB, Azure Storage to Aurora, DynamoDB, and S3 with CDC and zero downtime. - **Network and connectivity:** VPC, subnet, Direct Connect, or VPN planning. DNS, load balancer, and firewall migration. Cutover strategy with automatic rollback in case of issues. - **MAP assessment for migration:** Azure-to-AWS migrations can be assessed for the AWS MAP program. Credits may be available for qualifying migrations, subject to AWS criteria and approval. ### After migrating to AWS - **AWS Cost Optimization:** Optimize AWS costs after migration with FinOps. (/en/aws-cost-optimization) - **Secure Cloud Migration:** Security and compliance for your new AWS infrastructure. (/en/secure-cloud-migration) ### FAQ **Which workloads can migrate with low downtime?** It depends on architecture, database, dependencies, DNS, network, and business tolerance. We use replication, parallel operation, validation, and rollback where the workload allows. Zero downtime should not be promised before assessment. **Does Azure-to-AWS migration qualify for MAP credits?** Azure-to-AWS migrations can be assessed for the MAP program. Credits may be available for qualifying migrations, but depend on AWS criteria and approval. **How long does an Azure-to-AWS migration take?** Depends on scope. Assessment and service mapping takes 2-4 weeks. Pilot workload migration takes 4-8 weeks. Full migration can take 3-12 months, executed in waves to minimize risk. --- ## AWS cost optimization: FinOps consulting URL: https://elevata.io/en/aws-cost-optimization If your AWS bill is growing faster than the business, Elevata's FinOps consulting identifies waste across right-sizing, Savings Plans, Spot, and cost governance. The CredAluga case documents an approximately 55% reduction after an AWS migration. ### AWS costs growing without control Over-provisioned resources, idle instances, poorly planned commitments, and weak governance can represent a material share of the bill. Consulting is intended for engineering, finance, and leadership teams that need to restore visibility, reduce waste safely, or establish a continuous FinOps practice. ### CredAluga reduced monthly infrastructure cost by approximately 55% The Azure-to-AWS migration combined right-sizing, targeted high availability, EKS with Karpenter, and cost visibility by product and team. The result belongs to that context; every environment needs analysis based on real usage data before setting a target. ### How a cost recommendation should be documented #### Minimum fields - Current resource, utilization, recommendation, estimated savings, risk, owner, validation window, and rollback. - Impact on performance, availability, support, compliance, and operations. - Criteria to confirm the savings did not break the service. #### Beyond compute - Include data transfer, NAT Gateway, observability, logs, snapshots, storage tiering, dev/test environments, and AI traffic. - Continuous FinOps should become a monthly cadence between engineering and finance, not a one-off audit. ### How we optimize your AWS costs - **Assessment and quick wins:** Analysis of your current AWS bill to identify immediate waste: idle instances, unattached EBS, unused Elastic IPs, and over-provisioned resources. The initial focus is prioritizing low-risk savings opportunities before structural changes. - **Right-sizing and Savings Plans:** Instance right-sizing based on actual utilization. Recommendation and implementation of Savings Plans and Reserved Instances when usage patterns justify 1- to 3-year commitments. - **Spot and optimized architecture:** Spot Instances for interruption-tolerant workloads when the operational profile allows it. Architecture review to use serverless services (Lambda, Fargate) and storage tiers (S3 Intelligent-Tiering). - **Governance and continuous FinOps:** Cost dashboards with Cost Explorer and CUR. Budgets and automatic alerts. Tagging strategy for cost allocation by team, project, and environment. Monthly optimization reviews. ### Optimize telemetry cost too - **Reduce telemetry cost with Coralogix:** Compare platforms and prioritize telemetry according to operational value, retention, and cost. (/en/coralogix-observability) ### FAQ **How much can I save on my AWS bill?** Savings depend on workload maturity, utilization, existing commitments, and architecture. In many environments, the first assessment identifies low-risk opportunities in idle resources, right-sizing, storage, and commitment planning. For larger targets, we use CUR and Cost Explorer data before recommending changes. **What is FinOps?** FinOps is a practice that combines engineering, finance, and business to manage cloud costs. It includes real-time cost visibility, allocation by team/project, continuous optimization, and a culture of cost accountability. **Does right-sizing affect performance?** It should not when validated correctly. Right-sizing should use CPU, memory, network, I/O, performance targets, safety margin, validation window, and rollback. Changes without that evidence can create risk. **Does Elevata offer continuous optimization?** Yes. Beyond the initial assessment, we offer continuous FinOps as part of Elevata Orbit managed services. It includes monthly reviews, anomaly alerts, and Savings Plans updates as usage evolves. --- ## AWS Consulting in Brazil URL: https://elevata.io/en/aws-consulting-brazil Elevata helps Brazilian companies and international groups operating in Brazil when they need practical AWS guidance for migration, modernization, data, generative AI, security, and operations. The work combines Portuguese and English delivery, Brazilian regulatory context, and AWS São Paulo Region workload experience. - **Market:** Brazil - **Focus:** AWS Consulting - **AWS services:** Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Consulting in Brazil fits when the company already has delivery pressure but needs to reduce technical risk before scaling. In Brazil, the challenge is often combining scale, cost predictability, LGPD requirements, and collaboration across business, engineering, and global vendors. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Brazil? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Brazilian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. ### How to choose an AWS consulting partner in Brazil The strongest choice combines AWS validation, architecture depth, and clear thinking about cost, security, data, and operations. Search terms may vary, but the buying criteria do not. #### AWS validation and focus - Look for an AWS partner with public validation, but do not stop at the badge. Ask for examples across architecture, migration, modernization, data, AI, and operations. - Confirm the team can discuss Well-Architected, IAM, networking, observability, cost, and security by workload, not as a generic checklist. - If the search is for an AWS consultant, check whether the specialist has enough engineering support to deliver, not just advise. #### Real technical scope - Good AWS cloud consulting connects strategy to concrete decisions: accounts, Region, data, identity, network, CI/CD, observability, and FinOps. - In Brazil, include local integrations, workflow-level LGPD review, cost in local finance context, and Portuguese operating handoff. - Ask for deliverables that turn into work: target architecture, risks, backlog, owners, estimates, and production criteria. #### Signals of good delivery - The conversation starts with business goals, but quickly reaches technical constraints, dependencies, risk, and delivery sequence. - The plan separates what can be done remotely from what needs local workshops, executive alignment, or regulatory input. - After the assessment, it is clear who does what over the next 30, 60, and 90 days. ### How to make an AWS project in Brazil executable #### Brazil-specific points to put in the plan - Latency for Brazilian users, payment/tax integrations, Portuguese operations, and currency exposure in cloud cost. - sa-east-1 placement where it fits, with explicit decisions on DR, backups, logs, and sensitive data. - FinOps plan that translates technical usage into a budget conversation for engineering and finance. #### Proof that connects architecture to outcomes - Azure-to-AWS migration with documented cost reduction and stronger operational control. - Modernization with separated accounts, IAM, observability, and managed services to reduce operational risk. - Data and AI cases that require governance, security, and predictable cost before scaling usage. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Brazil. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Consulting variants - **AWS Consulting in São Paulo:** AWS Consulting in São Paulo with local context and workload-specific AWS architecture. (/en/aws-consulting-sao-paulo) - **Amazon Bedrock Consulting:** Turn GenAI use cases into Bedrock architecture plans ready for validation. (/en/amazon-bedrock-consulting) - **AWS Cost Optimization:** Connect cloud growth with FinOps governance based on real usage. (/en/aws-cost-optimization) - **Secure Cloud Migration:** Plan migration, security, and continuity before cutover. (/en/secure-cloud-migration) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Migration Acceleration Program: https://aws.amazon.com/migration-acceleration-program/ - AWS Well-Architected: https://aws.amazon.com/architecture/well-architected/ - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ ### FAQ **Does AWS Consulting in Brazil require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Brazil, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Consulting in Brazil?** A typical scope includes Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations. The final selection depends on the workload, available data, security requirements, operations, and cost. **AWS consulting, AWS consultant, or Amazon Web Services consulting: which term should you use?** These terms usually point to the same need: specialist help with AWS architecture, migration, modernization, cost, security, and AI. Use AWS consulting when you want a delivery team; use AWS consultant when you are comparing individual specialists. Elevata is an AWS Advanced Tier Services Partner with a senior team and bilingual delivery across Brazil and North America. **Can AWS Consulting in Brazil help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Consulting in São Paulo URL: https://elevata.io/en/aws-consulting-sao-paulo Elevata helps São Paulo companies, Brazilian technology teams, and groups with Brazil operations when they need practical AWS guidance for migration, modernization, data, generative AI, security, and operations. The work combines proximity to Brazil's largest business hub, bilingual delivery, and workload experience in the sa-east-1 Region. - **Market:** São Paulo - **Focus:** AWS Consulting - **AWS services:** Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Consulting in São Paulo fits when the company already has delivery pressure but needs to reduce technical risk before scaling. São Paulo companies often need to align leadership, technical teams, partners, and data requirements in a market with high pressure for speed. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in São Paulo? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For São Paulo teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. ### How to choose an AWS consulting partner in São Paulo When evaluating local consulting options, look for a team that can move decisions with leadership, product, security, and engineering without treating physical presence as a substitute for technical depth. #### Where proximity helps - Executive workshops, multi-team discovery, privacy decisions, local partner integrations, and Portuguese-language operations alignment. - Reviews where latency, sa-east-1, traffic, tax integrations, or local payments change the architecture. - Projects that need to connect a global parent, Brazilian operations, and distributed delivery teams. #### Criteria that matter more than address - AWS validation, experience in migration, modernization, data, AI, security, and FinOps, plus the ability to implement. - Clear deliverables: target architecture, risks, backlog, owners, estimates, production criteria, and a 30/60/90-day plan. - Ability to work in Portuguese and English when the operation spans Brazil, Canada, or an international parent company. ### What the assessment should produce in São Paulo #### Decision package - Inventory, current-state diagram, cost baseline, identity/network/security review, and data classification. - 30/60/90-day roadmap, prioritized backlog, risks, owners, and operating handoff. - Local decisions for São Paulo: Region, language, stakeholders, privacy, support, and operations. #### When São Paulo changes architecture Use a São Paulo page when aws consulting needs to address latency, local integrations, Portuguese-language operations, privacy requirements, and workload-specific use of sa-east-1. - Map which data, logs, backups, traces, and integrations need a local decision. - Document where global services or cross-Region profiles enter the design. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for São Paulo. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Consulting variants - **AWS Consulting in Brazil:** AWS Consulting in Brazil with local context and workload-specific AWS architecture. (/en/aws-consulting-brazil) - **Amazon Bedrock Consulting:** Turn GenAI use cases into Bedrock architecture plans ready for validation. (/en/amazon-bedrock-consulting) - **AWS Cost Optimization:** Connect cloud growth with FinOps governance based on real usage. (/en/aws-cost-optimization) - **Secure Cloud Migration:** Plan migration, security, and continuity before cutover. (/en/secure-cloud-migration) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Migration Acceleration Program: https://aws.amazon.com/migration-acceleration-program/ - AWS Well-Architected: https://aws.amazon.com/architecture/well-architected/ - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ ### FAQ **Does AWS Consulting in São Paulo require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For São Paulo, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Consulting in São Paulo?** A typical scope includes Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations. The final selection depends on the workload, available data, security requirements, operations, and cost. **AWS consulting, AWS consultant, or Amazon Web Services consulting: which term should you use?** These terms usually point to the same need: specialist help with AWS architecture, migration, modernization, cost, security, and AI. Use AWS consulting when you want a delivery team; use AWS consultant when you are comparing individual specialists. Elevata is an AWS Advanced Tier Services Partner with a senior team and bilingual delivery across Brazil and North America. **Can AWS Consulting in São Paulo help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## Amazon Bedrock Consulting in Canada URL: https://elevata.io/en/amazon-bedrock-consulting-canada Elevata helps Canadian companies and global teams operating in Canada when they want to use Amazon Bedrock for RAG, agents, Knowledge Bases, Guardrails, and enterprise-data integrations. The work combines Toronto presence, Canadian business-hours support, and bilingual delivery across Canada and Brazil. - **Market:** Canada - **Focus:** Amazon Bedrock Consulting - **AWS services:** RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control - **Residency and compliance:** Workload-specific ### Before choosing a Bedrock architecture - **Model, Region, and inference:** Which model family fits the task: Claude, Llama, Titan, or another Bedrock-supported model? Is the model available in the preferred Region, or does the workload require cross-Region inference? - **Retrieval or direct prompting:** Does the workflow need Bedrock Knowledge Bases, custom RAG, direct prompts, or no retrieval at all? Which data sources are authoritative and how will they stay current? - **Guardrails and application layer:** What should Guardrails handle, and what must be enforced in the application layer: permissions, PII handling, refusal behavior, tool limits, and human approval? - **Cost and quality by workflow:** How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality? ### When does this engagement make sense? Amazon Bedrock Consulting in Canada fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Canadian buyers often need to balance fast delivery, privacy, local business-hours operations, and integration with global teams without losing governance. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Canada? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Canadian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. ### What to decide before using Bedrock in Canada The assessment should define how the workload reaches production: use case, data, Region, permissions, evaluation, cost, and operations. #### Use case and stakeholders - Define the process: support, internal search, document review, contracts, payments, product, or operational workflow. - Include the process owner, engineering, data, security, privacy, finance, and operations when AI touches real systems. - Do not treat a demo with clean sample data as production proof. #### Region, data, and permissions - Document model, initiating Region, CRIS where applicable, prompts, completions, retrieved chunks, embeddings, logs, traces, and backups for Canada. - RAG must respect user, role, tenant, product, or geography permissions before retrieving context. - Guardrails help, but they do not replace application authorization, data minimization, audit, and human approval. #### Cost and launch - Measure cost per answer, ticket, document, user, or transaction before expanding usage. - Define evaluation set, usage limits, fallback, rollback, and owner for model and prompt changes. - The useful result is architecture, data-flow map, permission model, evaluation, cost guardrails, and implementation backlog. ### Bedrock workshop in Canada #### RAG checklist - Source inventory, document classification, chunking, metadata, permissions, citations, and content refresh. - Test whether users can retrieve only documents they could access directly. - Evaluate quality in the local language, domain terms, OCR, abbreviations, citations, and missing-data answers. #### Prototype metrics - Model, tokens, retrieved chunks, latency, errors, human correction rate, cost per task, and quality score. - Agent decision: approved APIs, limits, approvals, audit, fallback, and failure states. - Region and data record for Canada: prompts, completions, embeddings, traces, logs, backups, and evaluation. #### Canada beyond the keyword For Canadian companies, amazon bedrock consulting should cover privacy, Region, local-hours support, stakeholder workshops, and integration with distributed teams. - Define when Canada Central or Canada West matter for data, logs, backups, and operations. - Connect technical decisions to executive buyers, security, finance, and workload owners. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Canada. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore Amazon Bedrock Consulting variants - **Amazon Bedrock Consulting in Toronto:** Amazon Bedrock Consulting in Toronto with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-toronto) - **Amazon Bedrock Consulting in Brazil:** Amazon Bedrock Consulting in Brazil with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-brazil) - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Guardrails for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does Amazon Bedrock Consulting in Canada require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Canada, Elevata combines local presence when needed with senior remote delivery. **Which services are part of Amazon Bedrock Consulting in Canada?** A typical scope includes RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can Amazon Bedrock Consulting in Canada help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## Amazon Bedrock Consulting in Toronto URL: https://elevata.io/en/amazon-bedrock-consulting-toronto Elevata helps Toronto, GTA, and Canadian teams when they want to use Amazon Bedrock for RAG, agents, Knowledge Bases, Guardrails, and enterprise-data integrations. The work combines Toronto/GTA workshops, local contact, and Canadian business-hours delivery for technical decisions. - **Market:** Toronto - **Focus:** Amazon Bedrock Consulting - **AWS services:** RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control - **Residency and compliance:** Workload-specific ### Before choosing a Bedrock architecture - **Model, Region, and inference:** Which model family fits the task: Claude, Llama, Titan, or another Bedrock-supported model? Is the model available in the preferred Region, or does the workload require cross-Region inference? - **Retrieval or direct prompting:** Does the workflow need Bedrock Knowledge Bases, custom RAG, direct prompts, or no retrieval at all? Which data sources are authoritative and how will they stay current? - **Guardrails and application layer:** What should Guardrails handle, and what must be enforced in the application layer: permissions, PII handling, refusal behavior, tool limits, and human approval? - **Cost and quality by workflow:** How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality? ### When does this engagement make sense? Amazon Bedrock Consulting in Toronto fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Toronto teams often want direct access to senior architects for workshops, executive validation, and decisions that need to move in days, not months. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Toronto? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Toronto and GTA teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. Canada Central architectures can support Canadian privacy, continuity, and residency requirements when applicable. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. ### What to decide before using Bedrock in Toronto The assessment should define how the workload reaches production: use case, data, Region, permissions, evaluation, cost, and operations. #### Use case and stakeholders - Define the process: support, internal search, document review, contracts, payments, product, or operational workflow. - Include the process owner, engineering, data, security, privacy, finance, and operations when AI touches real systems. - Do not treat a demo with clean sample data as production proof. #### Region, data, and permissions - Document model, initiating Region, CRIS where applicable, prompts, completions, retrieved chunks, embeddings, logs, traces, and backups for Toronto. - RAG must respect user, role, tenant, product, or geography permissions before retrieving context. - Guardrails help, but they do not replace application authorization, data minimization, audit, and human approval. #### Cost and launch - Measure cost per answer, ticket, document, user, or transaction before expanding usage. - Define evaluation set, usage limits, fallback, rollback, and owner for model and prompt changes. - The useful result is architecture, data-flow map, permission model, evaluation, cost guardrails, and implementation backlog. ### Bedrock workshop in Toronto #### RAG checklist - Source inventory, document classification, chunking, metadata, permissions, citations, and content refresh. - Test whether users can retrieve only documents they could access directly. - Evaluate quality in the local language, domain terms, OCR, abbreviations, citations, and missing-data answers. #### Prototype metrics - Model, tokens, retrieved chunks, latency, errors, human correction rate, cost per task, and quality score. - Agent decision: approved APIs, limits, approvals, audit, fallback, and failure states. - Region and data record for Toronto: prompts, completions, embeddings, traces, logs, backups, and evaluation. #### When Toronto changes delivery Use a Toronto page when amazon bedrock consulting depends on in-person or hybrid workshops, GTA executive alignment, Canadian privacy decisions, or local-hours support. - Local agenda for leadership, product, security, finance, and architecture. - Explicit review of Canada Central, global integrations, and what can be remote. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Toronto. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore Amazon Bedrock Consulting variants - **Amazon Bedrock Consulting in Canada:** Amazon Bedrock Consulting in Canada with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Brazil:** Amazon Bedrock Consulting in Brazil with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-brazil) - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Guardrails for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does Amazon Bedrock Consulting in Toronto require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Toronto, Elevata combines local presence when needed with senior remote delivery. **Which services are part of Amazon Bedrock Consulting in Toronto?** A typical scope includes RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can Amazon Bedrock Consulting in Toronto help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. Canada Central architectures can support Canadian privacy, continuity, and residency requirements when applicable. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## Amazon Bedrock Consulting in Brazil URL: https://elevata.io/en/amazon-bedrock-consulting-brazil Elevata helps Brazilian companies and international groups operating in Brazil when they want to use Amazon Bedrock for RAG, agents, Knowledge Bases, Guardrails, and enterprise-data integrations. The work combines Portuguese and English delivery, Brazilian regulatory context, and AWS São Paulo Region workload experience. - **Market:** Brazil - **Focus:** Amazon Bedrock Consulting - **AWS services:** RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control - **Residency and compliance:** Workload-specific ### Before choosing a Bedrock architecture - **Model, Region, and inference:** Which model family fits the task: Claude, Llama, Titan, or another Bedrock-supported model? Is the model available in the preferred Region, or does the workload require cross-Region inference? - **Retrieval or direct prompting:** Does the workflow need Bedrock Knowledge Bases, custom RAG, direct prompts, or no retrieval at all? Which data sources are authoritative and how will they stay current? - **Guardrails and application layer:** What should Guardrails handle, and what must be enforced in the application layer: permissions, PII handling, refusal behavior, tool limits, and human approval? - **Cost and quality by workflow:** How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality? ### When does this engagement make sense? Amazon Bedrock Consulting in Brazil fits when the company already has delivery pressure but needs to reduce technical risk before scaling. In Brazil, the challenge is often combining scale, cost predictability, LGPD requirements, and collaboration across business, engineering, and global vendors. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Brazil? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Brazilian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. ### What to decide before using Bedrock in Brazil The assessment should define how the workload reaches production: use case, data, Region, permissions, evaluation, cost, and operations. #### Use case and stakeholders - Define the process: support, internal search, document review, contracts, payments, product, or operational workflow. - Include the process owner, engineering, data, security, privacy, finance, and operations when AI touches real systems. - Do not treat a demo with clean sample data as production proof. #### Region, data, and permissions - Document model, initiating Region, CRIS where applicable, prompts, completions, retrieved chunks, embeddings, logs, traces, and backups for Brazil. - RAG must respect user, role, tenant, product, or geography permissions before retrieving context. - Guardrails help, but they do not replace application authorization, data minimization, audit, and human approval. #### Cost and launch - Measure cost per answer, ticket, document, user, or transaction before expanding usage. - Define evaluation set, usage limits, fallback, rollback, and owner for model and prompt changes. - The useful result is architecture, data-flow map, permission model, evaluation, cost guardrails, and implementation backlog. ### Bedrock workshop in Brazil #### RAG checklist - Source inventory, document classification, chunking, metadata, permissions, citations, and content refresh. - Test whether users can retrieve only documents they could access directly. - Evaluate quality in the local language, domain terms, OCR, abbreviations, citations, and missing-data answers. #### Prototype metrics - Model, tokens, retrieved chunks, latency, errors, human correction rate, cost per task, and quality score. - Agent decision: approved APIs, limits, approvals, audit, fallback, and failure states. - Region and data record for Brazil: prompts, completions, embeddings, traces, logs, backups, and evaluation. #### Brazil beyond the keyword For Brazilian companies, amazon bedrock consulting should connect AWS architecture with use-case-specific LGPD review, local finance context, business integrations, and Portuguese-language operational handoff. - Include technology, privacy, finance, and operations stakeholders from the assessment. - Separate residency, latency, and continuity decisions by workload. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Brazil. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore Amazon Bedrock Consulting variants - **Amazon Bedrock Consulting in Canada:** Amazon Bedrock Consulting in Canada with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Toronto:** Amazon Bedrock Consulting in Toronto with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-toronto) - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Guardrails for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does Amazon Bedrock Consulting in Brazil require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Brazil, Elevata combines local presence when needed with senior remote delivery. **Which services are part of Amazon Bedrock Consulting in Brazil?** A typical scope includes RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can Amazon Bedrock Consulting in Brazil help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## Amazon Bedrock Consulting in São Paulo URL: https://elevata.io/en/amazon-bedrock-consulting-sao-paulo Elevata helps São Paulo companies, Brazilian technology teams, and groups with Brazil operations when they want to use Amazon Bedrock for RAG, agents, Knowledge Bases, Guardrails, and enterprise-data integrations. The work combines proximity to Brazil's largest business hub, bilingual delivery, and workload experience in the sa-east-1 Region. - **Market:** São Paulo - **Focus:** Amazon Bedrock Consulting - **AWS services:** RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control - **Residency and compliance:** Workload-specific ### Before choosing a Bedrock architecture - **Model, Region, and inference:** Which model family fits the task: Claude, Llama, Titan, or another Bedrock-supported model? Is the model available in the preferred Region, or does the workload require cross-Region inference? - **Retrieval or direct prompting:** Does the workflow need Bedrock Knowledge Bases, custom RAG, direct prompts, or no retrieval at all? Which data sources are authoritative and how will they stay current? - **Guardrails and application layer:** What should Guardrails handle, and what must be enforced in the application layer: permissions, PII handling, refusal behavior, tool limits, and human approval? - **Cost and quality by workflow:** How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality? ### When does this engagement make sense? Amazon Bedrock Consulting in São Paulo fits when the company already has delivery pressure but needs to reduce technical risk before scaling. São Paulo companies often need to align leadership, technical teams, partners, and data requirements in a market with high pressure for speed. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in São Paulo? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For São Paulo teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. ### What to decide before using Bedrock in São Paulo The assessment should define how the workload reaches production: use case, data, Region, permissions, evaluation, cost, and operations. #### Use case and stakeholders - Define the process: support, internal search, document review, contracts, payments, product, or operational workflow. - Include the process owner, engineering, data, security, privacy, finance, and operations when AI touches real systems. - Do not treat a demo with clean sample data as production proof. #### Region, data, and permissions - Document model, initiating Region, CRIS where applicable, prompts, completions, retrieved chunks, embeddings, logs, traces, and backups for São Paulo. - RAG must respect user, role, tenant, product, or geography permissions before retrieving context. - Guardrails help, but they do not replace application authorization, data minimization, audit, and human approval. #### Cost and launch - Measure cost per answer, ticket, document, user, or transaction before expanding usage. - Define evaluation set, usage limits, fallback, rollback, and owner for model and prompt changes. - The useful result is architecture, data-flow map, permission model, evaluation, cost guardrails, and implementation backlog. ### Bedrock workshop in São Paulo #### RAG checklist - Source inventory, document classification, chunking, metadata, permissions, citations, and content refresh. - Test whether users can retrieve only documents they could access directly. - Evaluate quality in the local language, domain terms, OCR, abbreviations, citations, and missing-data answers. #### Prototype metrics - Model, tokens, retrieved chunks, latency, errors, human correction rate, cost per task, and quality score. - Agent decision: approved APIs, limits, approvals, audit, fallback, and failure states. - Region and data record for São Paulo: prompts, completions, embeddings, traces, logs, backups, and evaluation. #### When São Paulo changes architecture Use a São Paulo page when amazon bedrock consulting needs to address latency, local integrations, Portuguese-language operations, privacy requirements, and workload-specific use of sa-east-1. - Map which data, logs, backups, traces, and integrations need a local decision. - Document where global services or cross-Region profiles enter the design. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for São Paulo. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore Amazon Bedrock Consulting variants - **Amazon Bedrock Consulting in Canada:** Amazon Bedrock Consulting in Canada with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Toronto:** Amazon Bedrock Consulting in Toronto with local context and workload-specific AWS architecture. (/en/amazon-bedrock-consulting-toronto) - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Guardrails for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does Amazon Bedrock Consulting in São Paulo require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For São Paulo, Elevata combines local presence when needed with senior remote delivery. **Which services are part of Amazon Bedrock Consulting in São Paulo?** A typical scope includes RAG and Knowledge Bases, Guardrails and security, Agents and automation, Usage-based cost control. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can Amazon Bedrock Consulting in São Paulo help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. For Bedrock workloads, we validate model availability, cross-Region inference profiles (CRIS), prompt and response routing, logs, backups, and customer controls before assuming any residency commitment. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Generative AI Consulting in Canada URL: https://elevata.io/en/aws-generative-ai-consulting-canada Elevata helps Canadian companies and global teams operating in Canada when they need to move AI prototypes into secure, operable products connected to company data. The work combines Toronto presence, Canadian business-hours support, and bilingual delivery across Canada and Brazil. - **Market:** Canada - **Focus:** AWS Generative AI Consulting - **AWS services:** Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps - **Residency and compliance:** Workload-specific ### Before choosing GenAI - **Is GenAI actually the right path?:** Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably? - **Current and permissioned sources:** Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate? - **What happens when the AI is wrong?:** Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions? - **Launch criteria:** Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook. ### When does this engagement make sense? AWS Generative AI Consulting in Canada fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Canadian buyers often need to balance fast delivery, privacy, local business-hours operations, and integration with global teams without losing governance. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Canada? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Canadian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. ### How to choose AWS GenAI in Canada The first step is deciding which workflow deserves to change, how success will be measured, and which risks need controls. #### Right workflow - Good candidates: support triage, internal search, document review, classification, summarization, routing, and operational assistance. - Avoid starting with poor data, undefined ownership, high risk, or expectation of full automation before proving value. - For Canadian teams, validate language, stakeholders, privacy requirements, Region, and systems that need integration. #### Architecture patterns - Simple prompt workflows for classification, summarization, and extraction with limited proprietary context. - RAG when answers depend on documents and permissions; agents or MCP when AI needs to query tools or APIs. - Human-in-the-loop when the final decision needs human approval because of risk, customer impact, or compliance. #### Discovery deliverables - Use-case ranking by value, data readiness, feasibility, risk, evaluation clarity, and cost predictability. - Target architecture, data sources, quality metrics, unit cost, owners, and first-sprint backlog. - Explicit criteria to decide whether the POC should scale or stop. ### From GenAI idea to backlog in Canada #### Workshop decisions - Target process, owner, users, integrated systems, data required, risk, and automation boundary. - Pattern: simple prompt, RAG, agent/MCP, human-in-the-loop, SageMaker, or data platform first. - Metrics: groundedness, accuracy, tool-call accuracy, safe failure, latency, cost, and human effort. #### What not to build yet - Full automation where there is not yet reliable data, owner, evaluation, or error tolerance. - Agent with real tool calls before authentication, limits, approval, audit, and rollback exist. - Sophisticated model to compensate for stale, duplicate, or poorly permissioned content. #### Canada beyond the keyword For Canadian companies, aws generative ai consulting should cover privacy, Region, local-hours support, stakeholder workshops, and integration with distributed teams. - Define when Canada Central or Canada West matter for data, logs, backups, and operations. - Connect technical decisions to executive buyers, security, finance, and workload owners. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Canada. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Generative AI Consulting variants - **AWS Generative AI Consulting in Toronto:** AWS Generative AI Consulting in Toronto with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-toronto) - **AWS Generative AI Consulting in Brazil:** AWS Generative AI Consulting in Brazil with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-brazil) - **AWS Generative AI Consulting in Canada:** Strategy, RAG, agents, and governance for Canadian teams. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Brazil:** GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region. (/en/aws-generative-ai-consulting-brazil) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does AWS Generative AI Consulting in Canada require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Canada, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Generative AI Consulting in Canada?** A typical scope includes Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Generative AI Consulting in Canada help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Generative AI Consulting in Toronto URL: https://elevata.io/en/aws-generative-ai-consulting-toronto Elevata helps Toronto, GTA, and Canadian teams when they need to move AI prototypes into secure, operable products connected to company data. The work combines Toronto/GTA workshops, local contact, and Canadian business-hours delivery for technical decisions. - **Market:** Toronto - **Focus:** AWS Generative AI Consulting - **AWS services:** Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps - **Residency and compliance:** Workload-specific ### Before choosing GenAI - **Is GenAI actually the right path?:** Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably? - **Current and permissioned sources:** Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate? - **What happens when the AI is wrong?:** Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions? - **Launch criteria:** Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook. ### When does this engagement make sense? AWS Generative AI Consulting in Toronto fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Toronto teams often want direct access to senior architects for workshops, executive validation, and decisions that need to move in days, not months. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Toronto? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Toronto and GTA teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. Canada Central architectures can support Canadian privacy, continuity, and residency requirements when applicable. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. ### How to choose AWS GenAI in Toronto The first step is deciding which workflow deserves to change, how success will be measured, and which risks need controls. #### Right workflow - Good candidates: support triage, internal search, document review, classification, summarization, routing, and operational assistance. - Avoid starting with poor data, undefined ownership, high risk, or expectation of full automation before proving value. - For Toronto and GTA teams, validate language, stakeholders, privacy requirements, Region, and systems that need integration. #### Architecture patterns - Simple prompt workflows for classification, summarization, and extraction with limited proprietary context. - RAG when answers depend on documents and permissions; agents or MCP when AI needs to query tools or APIs. - Human-in-the-loop when the final decision needs human approval because of risk, customer impact, or compliance. #### Discovery deliverables - Use-case ranking by value, data readiness, feasibility, risk, evaluation clarity, and cost predictability. - Target architecture, data sources, quality metrics, unit cost, owners, and first-sprint backlog. - Explicit criteria to decide whether the POC should scale or stop. ### From GenAI idea to backlog in Toronto #### Workshop decisions - Target process, owner, users, integrated systems, data required, risk, and automation boundary. - Pattern: simple prompt, RAG, agent/MCP, human-in-the-loop, SageMaker, or data platform first. - Metrics: groundedness, accuracy, tool-call accuracy, safe failure, latency, cost, and human effort. #### What not to build yet - Full automation where there is not yet reliable data, owner, evaluation, or error tolerance. - Agent with real tool calls before authentication, limits, approval, audit, and rollback exist. - Sophisticated model to compensate for stale, duplicate, or poorly permissioned content. #### When Toronto changes delivery Use a Toronto page when aws generative ai consulting depends on in-person or hybrid workshops, GTA executive alignment, Canadian privacy decisions, or local-hours support. - Local agenda for leadership, product, security, finance, and architecture. - Explicit review of Canada Central, global integrations, and what can be remote. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Toronto. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Generative AI Consulting variants - **AWS Generative AI Consulting in Canada:** AWS Generative AI Consulting in Canada with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Brazil:** AWS Generative AI Consulting in Brazil with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-brazil) - **AWS Generative AI Consulting in Canada:** Strategy, RAG, agents, and governance for Canadian teams. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Brazil:** GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region. (/en/aws-generative-ai-consulting-brazil) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does AWS Generative AI Consulting in Toronto require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Toronto, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Generative AI Consulting in Toronto?** A typical scope includes Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Generative AI Consulting in Toronto help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. Canada Central architectures can support Canadian privacy, continuity, and residency requirements when applicable. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Generative AI Consulting in Brazil URL: https://elevata.io/en/aws-generative-ai-consulting-brazil Elevata helps Brazilian companies and international groups operating in Brazil when they need to move AI prototypes into secure, operable products connected to company data. The work combines Portuguese and English delivery, Brazilian regulatory context, and AWS São Paulo Region workload experience. - **Market:** Brazil - **Focus:** AWS Generative AI Consulting - **AWS services:** Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps - **Residency and compliance:** Workload-specific ### Before choosing GenAI - **Is GenAI actually the right path?:** Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably? - **Current and permissioned sources:** Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate? - **What happens when the AI is wrong?:** Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions? - **Launch criteria:** Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook. ### When does this engagement make sense? AWS Generative AI Consulting in Brazil fits when the company already has delivery pressure but needs to reduce technical risk before scaling. In Brazil, the challenge is often combining scale, cost predictability, LGPD requirements, and collaboration across business, engineering, and global vendors. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Brazil? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Brazilian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. ### How to choose AWS GenAI in Brazil The first step is deciding which workflow deserves to change, how success will be measured, and which risks need controls. #### Right workflow - Good candidates: support triage, internal search, document review, classification, summarization, routing, and operational assistance. - Avoid starting with poor data, undefined ownership, high risk, or expectation of full automation before proving value. - For Brazilian teams, validate language, stakeholders, privacy requirements, Region, and systems that need integration. #### Architecture patterns - Simple prompt workflows for classification, summarization, and extraction with limited proprietary context. - RAG when answers depend on documents and permissions; agents or MCP when AI needs to query tools or APIs. - Human-in-the-loop when the final decision needs human approval because of risk, customer impact, or compliance. #### Discovery deliverables - Use-case ranking by value, data readiness, feasibility, risk, evaluation clarity, and cost predictability. - Target architecture, data sources, quality metrics, unit cost, owners, and first-sprint backlog. - Explicit criteria to decide whether the POC should scale or stop. ### From GenAI idea to backlog in Brazil #### Workshop decisions - Target process, owner, users, integrated systems, data required, risk, and automation boundary. - Pattern: simple prompt, RAG, agent/MCP, human-in-the-loop, SageMaker, or data platform first. - Metrics: groundedness, accuracy, tool-call accuracy, safe failure, latency, cost, and human effort. #### What not to build yet - Full automation where there is not yet reliable data, owner, evaluation, or error tolerance. - Agent with real tool calls before authentication, limits, approval, audit, and rollback exist. - Sophisticated model to compensate for stale, duplicate, or poorly permissioned content. #### Brazil beyond the keyword For Brazilian companies, aws generative ai consulting should connect AWS architecture with use-case-specific LGPD review, local finance context, business integrations, and Portuguese-language operational handoff. - Include technology, privacy, finance, and operations stakeholders from the assessment. - Separate residency, latency, and continuity decisions by workload. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Brazil. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Generative AI Consulting variants - **AWS Generative AI Consulting in Canada:** AWS Generative AI Consulting in Canada with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Toronto:** AWS Generative AI Consulting in Toronto with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-toronto) - **AWS Generative AI Consulting in Canada:** Strategy, RAG, agents, and governance for Canadian teams. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Brazil:** GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region. (/en/aws-generative-ai-consulting-brazil) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does AWS Generative AI Consulting in Brazil require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Brazil, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Generative AI Consulting in Brazil?** A typical scope includes Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Generative AI Consulting in Brazil help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Generative AI Consulting in São Paulo URL: https://elevata.io/en/aws-generative-ai-consulting-sao-paulo Elevata helps São Paulo companies, Brazilian technology teams, and groups with Brazil operations when they need to move AI prototypes into secure, operable products connected to company data. The work combines proximity to Brazil's largest business hub, bilingual delivery, and workload experience in the sa-east-1 Region. - **Market:** São Paulo - **Focus:** AWS Generative AI Consulting - **AWS services:** Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps - **Residency and compliance:** Workload-specific ### Before choosing GenAI - **Is GenAI actually the right path?:** Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably? - **Current and permissioned sources:** Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate? - **What happens when the AI is wrong?:** Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions? - **Launch criteria:** Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook. ### When does this engagement make sense? AWS Generative AI Consulting in São Paulo fits when the company already has delivery pressure but needs to reduce technical risk before scaling. São Paulo companies often need to align leadership, technical teams, partners, and data requirements in a market with high pressure for speed. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in São Paulo? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For São Paulo teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. ### How to choose AWS GenAI in São Paulo The first step is deciding which workflow deserves to change, how success will be measured, and which risks need controls. #### Right workflow - Good candidates: support triage, internal search, document review, classification, summarization, routing, and operational assistance. - Avoid starting with poor data, undefined ownership, high risk, or expectation of full automation before proving value. - For São Paulo teams, validate language, stakeholders, privacy requirements, Region, and systems that need integration. #### Architecture patterns - Simple prompt workflows for classification, summarization, and extraction with limited proprietary context. - RAG when answers depend on documents and permissions; agents or MCP when AI needs to query tools or APIs. - Human-in-the-loop when the final decision needs human approval because of risk, customer impact, or compliance. #### Discovery deliverables - Use-case ranking by value, data readiness, feasibility, risk, evaluation clarity, and cost predictability. - Target architecture, data sources, quality metrics, unit cost, owners, and first-sprint backlog. - Explicit criteria to decide whether the POC should scale or stop. ### From GenAI idea to backlog in São Paulo #### Workshop decisions - Target process, owner, users, integrated systems, data required, risk, and automation boundary. - Pattern: simple prompt, RAG, agent/MCP, human-in-the-loop, SageMaker, or data platform first. - Metrics: groundedness, accuracy, tool-call accuracy, safe failure, latency, cost, and human effort. #### What not to build yet - Full automation where there is not yet reliable data, owner, evaluation, or error tolerance. - Agent with real tool calls before authentication, limits, approval, audit, and rollback exist. - Sophisticated model to compensate for stale, duplicate, or poorly permissioned content. #### When São Paulo changes architecture Use a São Paulo page when aws generative ai consulting needs to address latency, local integrations, Portuguese-language operations, privacy requirements, and workload-specific use of sa-east-1. - Map which data, logs, backups, traces, and integrations need a local decision. - Document where global services or cross-Region profiles enter the design. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for São Paulo. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Generative AI Consulting variants - **AWS Generative AI Consulting in Canada:** AWS Generative AI Consulting in Canada with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Toronto:** AWS Generative AI Consulting in Toronto with local context and workload-specific AWS architecture. (/en/aws-generative-ai-consulting-toronto) - **AWS Generative AI Consulting in Canada:** Strategy, RAG, agents, and governance for Canadian teams. (/en/aws-generative-ai-consulting-canada) - **AWS Generative AI Consulting in Brazil:** GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region. (/en/aws-generative-ai-consulting-brazil) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html ### FAQ **Does AWS Generative AI Consulting in São Paulo require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For São Paulo, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Generative AI Consulting in São Paulo?** A typical scope includes Strategy and use cases, RAG, agents, and MCP, Bedrock and SageMaker, AI governance and FinOps. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Generative AI Consulting in São Paulo help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. For generative AI, residency decisions depend on the model, service, Region, inference profile, data sent to the model, logs, backups, and customer-defined operating controls. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Cost Optimization in Canada URL: https://elevata.io/en/aws-cost-optimization-canada Elevata helps Canadian companies and global teams operating in Canada when they need to understand waste, commitments, architecture, tags, budgets, and savings opportunities without risking performance. The work combines Toronto presence, Canadian business-hours support, and bilingual delivery across Canada and Brazil. - **Market:** Canada - **Focus:** AWS Cost Optimization - **AWS services:** CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Cost Optimization in Canada fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Canadian buyers often need to balance fast delivery, privacy, local business-hours operations, and integration with global teams without losing governance. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Canada? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Canadian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. ### How to assess AWS cost in Canada #### Baseline data - CUR or Cost Explorer, tags, accounts, environments, owners, seasonality, existing commitments, and planned changes. - Separation of production, dev/test, logs, data transfer, NAT Gateway, storage, snapshots, and observability. - Target by unit: product, customer, environment, transaction, or workload. #### Low-risk quick wins - Idle resources, old EBS/snapshots, Elastic IPs, forgotten environments, logs without retention, and storage tiering. - Right-sizing only after reviewing utilization, performance targets, safety margin, validation window, and rollback. - Commitments only when workload direction, seasonality, Region, and instance family justify the term. ### What the assessment should produce in Canada #### Decision package - Inventory, current-state diagram, cost baseline, identity/network/security review, and data classification. - 30/60/90-day roadmap, prioritized backlog, risks, owners, and operating handoff. - Local decisions for Canada: Region, language, stakeholders, privacy, support, and operations. #### Canada beyond the keyword For Canadian companies, aws cost optimization should cover privacy, Region, local-hours support, stakeholder workshops, and integration with distributed teams. - Define when Canada Central or Canada West matter for data, logs, backups, and operations. - Connect technical decisions to executive buyers, security, finance, and workload owners. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Canada. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Cost Optimization variants - **AWS Cost Optimization in Brazil:** AWS Cost Optimization in Brazil with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-brazil) - **AWS Cost Optimization in São Paulo:** AWS Cost Optimization in São Paulo with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-sao-paulo) - **AI Inference Cost Optimization:** Control Bedrock, SageMaker, and production AI workload cost. (/en/aws-ai-inference-cost-optimization) - **Amazon Bedrock Cost Optimization:** Design model usage, prompts, caching, and cost observability. (/en/amazon-bedrock-cost-optimization) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Cost Optimization: https://aws.amazon.com/aws-cost-management/aws-cost-optimization/ - AWS Cost Explorer: https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html - AWS Cost and Usage Report: https://docs.aws.amazon.com/cur/latest/userguide/what-is-cur.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 ### FAQ **Does AWS Cost Optimization in Canada require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Canada, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Cost Optimization in Canada?** A typical scope includes CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Cost Optimization in Canada help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The Canada Central Region and multi-Region architecture patterns can support residency, privacy, and continuity requirements when the workload calls for that strategy. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Cost Optimization in Brazil URL: https://elevata.io/en/aws-cost-optimization-brazil Elevata helps Brazilian companies and international groups operating in Brazil when they need to understand waste, commitments, architecture, tags, budgets, and savings opportunities without risking performance. The work combines Portuguese and English delivery, Brazilian regulatory context, and AWS São Paulo Region workload experience. - **Market:** Brazil - **Focus:** AWS Cost Optimization - **AWS services:** CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Cost Optimization in Brazil fits when the company already has delivery pressure but needs to reduce technical risk before scaling. In Brazil, the challenge is often combining scale, cost predictability, LGPD requirements, and collaboration across business, engineering, and global vendors. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in Brazil? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Brazilian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. ### How to assess AWS cost in Brazil #### Baseline data - CUR or Cost Explorer, tags, accounts, environments, owners, seasonality, existing commitments, and planned changes. - Separation of production, dev/test, logs, data transfer, NAT Gateway, storage, snapshots, and observability. - Target by unit: product, customer, environment, transaction, or workload. #### Low-risk quick wins - Idle resources, old EBS/snapshots, Elastic IPs, forgotten environments, logs without retention, and storage tiering. - Right-sizing only after reviewing utilization, performance targets, safety margin, validation window, and rollback. - Commitments only when workload direction, seasonality, Region, and instance family justify the term. ### What the assessment should produce in Brazil #### Decision package - Inventory, current-state diagram, cost baseline, identity/network/security review, and data classification. - 30/60/90-day roadmap, prioritized backlog, risks, owners, and operating handoff. - Local decisions for Brazil: Region, language, stakeholders, privacy, support, and operations. #### Brazil beyond the keyword For Brazilian companies, aws cost optimization should connect AWS architecture with use-case-specific LGPD review, local finance context, business integrations, and Portuguese-language operational handoff. - Include technology, privacy, finance, and operations stakeholders from the assessment. - Separate residency, latency, and continuity decisions by workload. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Brazil. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Cost Optimization variants - **AWS Cost Optimization in Canada:** AWS Cost Optimization in Canada with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-canada) - **AWS Cost Optimization in São Paulo:** AWS Cost Optimization in São Paulo with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-sao-paulo) - **AI Inference Cost Optimization:** Control Bedrock, SageMaker, and production AI workload cost. (/en/aws-ai-inference-cost-optimization) - **Amazon Bedrock Cost Optimization:** Design model usage, prompts, caching, and cost observability. (/en/amazon-bedrock-cost-optimization) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Cost Optimization: https://aws.amazon.com/aws-cost-management/aws-cost-optimization/ - AWS Cost Explorer: https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html - AWS Cost and Usage Report: https://docs.aws.amazon.com/cur/latest/userguide/what-is-cur.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ ### FAQ **Does AWS Cost Optimization in Brazil require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Brazil, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Cost Optimization in Brazil?** A typical scope includes CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Cost Optimization in Brazil help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The AWS São Paulo Region can support Brazil data-residency strategies and LGPD-aligned architecture patterns when workloads require local processing or storage. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS Cost Optimization in São Paulo URL: https://elevata.io/en/aws-cost-optimization-sao-paulo Elevata helps São Paulo companies, Brazilian technology teams, and groups with Brazil operations when they need to understand waste, commitments, architecture, tags, budgets, and savings opportunities without risking performance. The work combines proximity to Brazil's largest business hub, bilingual delivery, and workload experience in the sa-east-1 Region. - **Market:** São Paulo - **Focus:** AWS Cost Optimization - **AWS services:** CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Cost Optimization in São Paulo fits when the company already has delivery pressure but needs to reduce technical risk before scaling. São Paulo companies often need to align leadership, technical teams, partners, and data requirements in a market with high pressure for speed. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work in São Paulo? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For São Paulo teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. ### How to assess AWS cost in São Paulo #### Baseline data - CUR or Cost Explorer, tags, accounts, environments, owners, seasonality, existing commitments, and planned changes. - Separation of production, dev/test, logs, data transfer, NAT Gateway, storage, snapshots, and observability. - Target by unit: product, customer, environment, transaction, or workload. #### Low-risk quick wins - Idle resources, old EBS/snapshots, Elastic IPs, forgotten environments, logs without retention, and storage tiering. - Right-sizing only after reviewing utilization, performance targets, safety margin, validation window, and rollback. - Commitments only when workload direction, seasonality, Region, and instance family justify the term. ### What the assessment should produce in São Paulo #### Decision package - Inventory, current-state diagram, cost baseline, identity/network/security review, and data classification. - 30/60/90-day roadmap, prioritized backlog, risks, owners, and operating handoff. - Local decisions for São Paulo: Region, language, stakeholders, privacy, support, and operations. #### When São Paulo changes architecture Use a São Paulo page when aws cost optimization needs to address latency, local integrations, Portuguese-language operations, privacy requirements, and workload-specific use of sa-east-1. - Map which data, logs, backups, traces, and integrations need a local decision. - Document where global services or cross-Region profiles enter the design. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for São Paulo. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### Explore AWS Cost Optimization variants - **AWS Cost Optimization in Canada:** AWS Cost Optimization in Canada with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-canada) - **AWS Cost Optimization in Brazil:** AWS Cost Optimization in Brazil with local context and workload-specific AWS architecture. (/en/aws-cost-optimization-brazil) - **AI Inference Cost Optimization:** Control Bedrock, SageMaker, and production AI workload cost. (/en/aws-ai-inference-cost-optimization) - **Amazon Bedrock Cost Optimization:** Design model usage, prompts, caching, and cost observability. (/en/amazon-bedrock-cost-optimization) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Cost Optimization: https://aws.amazon.com/aws-cost-management/aws-cost-optimization/ - AWS Cost Explorer: https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html - AWS Cost and Usage Report: https://docs.aws.amazon.com/cur/latest/userguide/what-is-cur.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ ### FAQ **Does AWS Cost Optimization in São Paulo require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For São Paulo, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Cost Optimization in São Paulo?** A typical scope includes CUR assessment, Right-sizing, Savings Plans and RI, FinOps governance. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Cost Optimization in São Paulo help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. The sa-east-1 Region can support Brazil data-residency strategies and compliance requirements when the technical design calls for local resources. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- ## AWS AI Inference Cost Optimization URL: https://elevata.io/en/aws-ai-inference-cost-optimization Elevata helps teams reduce AI cost unpredictability on AWS by measuring real usage, choosing fit-for-purpose models, and designing controls before scaling traffic. - **Services:** Bedrock, SageMaker, Lambda, EKS - **Data:** CUR + application logs - **Risk:** ungoverned traffic and prompts - **Outcome:** cost per task ### AI costs grow differently AI workloads combine tokens, model calls, embeddings, vector search, logs, storage, orchestration, and application infrastructure. Small prompt, context, and routing decisions can change unit cost. That is why optimization must connect architecture, product, and FinOps. ### Start with unit cost The analysis creates metrics such as cost per summary, search, recommendation, ticket, or transaction. Then we assess model choice, context size, caching, batching, limits, fallback, storage, and observability to reduce waste without degrading quality. ### How to estimate and reduce AI unit cost Start with the right unit economics: cost per answer, document, ticket, search, recommendation, or transaction. #### Practical formula - Cost per task = input tokens + output tokens + embeddings + vector search + orchestration + logs + retries + human review where applicable. - Measure by product workflow: answer, user/month, document, ticket, recommendation, successful automation, or transaction. - Compare cost together with quality, latency, and error rate; cost alone leads to cheap models that fail more often. #### Data required - CUR or Cost Explorer, model IDs, input/output token counts, calls per user action, and embedding/vector-store cost. - Retrieved context size, retry/fallback/cache-hit rates, latency, errors, quality scores, and user or tenant IDs where appropriate. - Quality criteria: correct answers, groundedness, safety, latency, and human effort after the response. #### Optimization sequence - First remove duplicate calls, excessive context, and prompts that bring irrelevant data. - Then apply model routing, caching, batching where real-time response is not required, environment limits, and fallback with objective criteria. - Only then consider dedicated hosting or changing the stack; without measurement, this can add complexity without reducing cost. ### How an AI cost review becomes action #### Example: support-ticket summarization A workflow processes 10,000 tickets per month and triggers three model calls per ticket: classification, summary, and recommended response. Agents use the recommended response only 30% of the time. - Classify and summarize every ticket, but generate recommended responses only when the agent requests them. - Use a smaller model for classification, cap retrieved customer-history context, and cache summaries for reopened tickets. - Deliverables: unit-cost dashboard, prioritized optimization backlog, quality tests, and operating guardrails. #### When not to optimize yet - There is no real or near-real traffic to measure. - There is no quality target or representative evaluation set. - No owner can approve routing, caching, fallback, or user-limit changes. ### How we optimize inference cost - **Business-unit cost measurement:** We connect CUR, logs, and product metrics to understand cost per task, customer, workflow, and model. - **Model routing and evaluation:** We compare models and fallback strategies using quality, latency, security, and cost criteria. - **Prompts, context, and caching:** We reduce unnecessary context, duplicate calls, and recomputation with caching patterns and selective retrieval. - **Operational guardrails:** We define budgets, limits, alerts, environment policies, and playbooks to control usage spikes. ### Go deeper on AI cost strategy - **Amazon Bedrock Cost Optimization:** Control model, prompt, and RAG cost on Bedrock. (/en/amazon-bedrock-cost-optimization) - **AWS Generative AI Consulting:** Strategy and architecture for generative AI on AWS. (/en/aws-generative-ai-consulting) - **MCP + RAG travel case:** See production AI with Bedrock, MCP, and RAG. (/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws) ### Technical sources - Amazon Bedrock pricing: https://aws.amazon.com/bedrock/pricing/ - Amazon SageMaker pricing: https://aws.amazon.com/sagemaker/pricing/ - AWS Cost and Usage Report: https://docs.aws.amazon.com/cur/latest/userguide/what-is-cur.html ### FAQ **How do you reduce Bedrock inference cost?** Start by measuring cost per task. Then tune model selection, context size, caching, chunking, retrieval filters, usage limits, and fallback. Recommendations should be validated against quality and latency, not just price. **Is Bedrock or SageMaker cheaper?** It depends on usage pattern, model, volume, latency, and operational requirements. Bedrock often fits managed model usage; SageMaker can fit when you need more control over training, tuning, or hosting. The comparison needs workload data. **Can I optimize cost without hurting quality?** Yes, when optimization uses quality tests and workflow-level metrics. Many savings come from reducing redundant calls, excessive context, and missing caching, not from switching to a worse model. --- ## Amazon Bedrock Cost Optimization URL: https://elevata.io/en/amazon-bedrock-cost-optimization Elevata helps teams design Bedrock applications with predictable cost by connecting prompts, RAG, models, metrics, and budgets before usage scales. - **Model:** task-based selection - **Context:** selective RAG - **Control:** budgets and limits - **Metric:** cost per answer ### Bedrock costs more when usage design is missing Bedrock cost does not depend only on the model. Prompt size, retrieved context, number of calls, repetition, fallback, logs, and test traffic also matter. Optimization starts with task-level measurement and clear quality criteria. ### FinOps needs to start before launch Bedrock projects should launch with environment limits, unit-cost metrics, alerts, useful logs, and clear workflow ownership. That reduces surprises when real users begin using the product. ### Where Bedrock cost actually changes Bedrock does not become expensive only because of model choice. Usage design decides how much context, repetition, testing, and fallback enter the bill. #### Primary levers - Model selection by task: simple classification, extraction, synthesis, and dense reasoning do not need the same model. - Prompt size, instruction compression, and retrieved context: every irrelevant chunk increases cost and can worsen the answer. - Caching, routing, batching, environment limits, and test-traffic controls reduce unnecessary recomputation. #### Before optimizing - Separate cost by workflow, feature, customer, tenant, model, and environment: chat, RAG, document analysis, agent, batch, and test. - Have a quality benchmark and evaluation set to validate savings without degrading answer quality, latency, or trust. - Map budgets, owners, alerts, and monthly review rhythm before releasing to real users. #### Common mistakes - Using the strongest model as the default for every task. - Retrieving too much context in RAG to compensate for missing evaluation. - Optimizing only token price without measuring latency, retries, hallucination, and human effort. ### Choices that change Bedrock cost #### Model, throughput, and context - Use smaller models for classification, extraction, and normalization; keep evals to catch quality loss. - Provisioned throughput fits stable high-volume workloads; on-demand fits early or spiky workloads. - Cross-Region inference profiles can help capacity, but need latency, residency, and compliance review. #### Control layer before Bedrock - Classify the request, look up tenant budget, choose model, and cap tokens before calling the model. - Separate cost by feature, tenant, model, and environment so engineering and finance see the same unit economics. - Record operational metadata by default; avoid storing sensitive prompt bodies without a clear need. ### What we review in Bedrock applications - **Prompt and context architecture:** We review templates, chunking, filters, context size, and retrieval to reduce unnecessary tokens. - **Model selection and routing:** We define when to use different models, fallback, and evaluation by quality, latency, and cost. - **Cost observability:** We connect application logs, product metrics, tags, and financial data to measure cost by workflow. - **Budgets and operations:** We create alerts, limits, spike playbooks, and periodic reviews to keep cost and quality under control. ### Continue through AI architecture - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Brazil:** Bedrock architecture with attention to LGPD, logs, and the São Paulo Region. (/en/amazon-bedrock-consulting-brazil) - **Amazon Bedrock Cost Optimization:** Control model, prompt, and RAG cost on Bedrock. (/en/amazon-bedrock-cost-optimization) - **Claude on Bedrock for Canada:** Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads. (/en/claude-on-amazon-bedrock-canada) ### Technical sources - Amazon Bedrock pricing: https://aws.amazon.com/bedrock/pricing/ - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html ### FAQ **How is Amazon Bedrock billed?** Billing depends on the feature and model used. For generative applications, we usually assess calls, tokens, embeddings, Knowledge Bases, traffic, and supporting resources. Use the official AWS pricing page to confirm current rates. **Does RAG increase Bedrock cost?** It can increase cost if it retrieves too much context or makes duplicate calls. It can also reduce cost when it improves accuracy and avoids repeated attempts. Chunking, filters, caching, and evaluation determine the result. **When should I optimize Bedrock?** Before moving from pilot to production. At that point there are enough prompts, users, and metrics to measure unit cost, but it is still easy to correct architecture and governance. --- ## Claude Code on Amazon Bedrock URL: https://elevata.io/en/claude-code-on-bedrock Review the right path for using Claude Code with AWS controls: CLI, Claude Desktop/Cowork with Bedrock-backed inference in Research Preview, or a hybrid model. - **AWS setup:** Model, Region, quota - **Control:** IAM, SSO, MDM - **Evidence:** Observability and cost ### What to review before giving more people access If these points do not yet have an owner and clear criteria, keep usage inside a small pilot. - **Model, Region, quota, and inference:** Validate available Anthropic models, inference profiles, streaming quota, first-use permissions, and SCP/IAM policies that can block calls. - **Credentials, refresh, and user attribution:** Decide between testing API keys, AWS login, IAM Identity Center, or direct IdP integration; each option changes security, MFA, auditability, cost, and developer experience. - **Repositories, commands, data, and human review:** Control repositories, folders, tools, egress, secret handling, allowed data, and change approval before opening sensitive code. - **Cost, support, and measurement:** Measure adoption, cost, latency, failures, tokens, team usage, and engineering workflow impact before expanding to more users. ### For teams moving beyond one-developer setup This review is for CTOs, platform, security, and engineering leaders moving from one developer test to team use, without creating one-off exceptions for IAM, credentials, repositories, logs, cost, and support. ### Bedrock is not always the simplest path If the company does not need AWS controls, AWS-side billing, or IAM-based access, Claude Team or Enterprise may be simpler. If request-level policy, multi-provider routing, or custom middleware is required, a gateway may fit. The review makes those choices clear. ### Choose the right setup before standardizing | Claude Code CLI on Bedrock | Claude Desktop/Cowork with third-party inference | |---|---| | Engineers working in terminal, IDE, repositories, and automation workflows. | Teams that need a desktop experience, delegated work, local files, MDM, and Bedrock-backed inference. | | Login wizard or environment variables, settings, AWS_REGION, AWS_PROFILE, credentials, and pinned models. | System/MDM configuration, inferenceProvider=bedrock, local credentials, models, Research Preview status, and Code tab validation. | | IAM, CloudTrail, OpenTelemetry, allowed repositories, commands, and Claude Code policies. | MDM, egress, workspace folders, local observability, and separate plugin/MCP validation. | | Credentials expire, the model is unavailable, AWS_REGION is not explicit, or IAM becomes too broad. | Incomplete MDM config, policy does not propagate to the Code tab, local files were not reviewed, or egress was not approved. | ### Choose how Claude Code should run The decision is not only CLI versus desktop. The right path depends on identity, AWS controls, cost attribution, developer UX, data requirements, and request-level policy. #### Use Bedrock when - The company wants inference, billing, audit, quotas, and access policy inside AWS. - IAM, SCPs, AWS Organizations, CloudTrail, OpenTelemetry, and Cost Explorer already shape platform controls. - The pilot needs evidence for security, finance, and engineering before it expands. #### Consider Claude Team or Enterprise when - Anthropic's managed SaaS path better fits administration, identity, billing, and user experience. - The company does not need Bedrock to control inference for this use case. - The priority is reducing operational complexity, not integrating the pilot into existing AWS controls. #### Consider a gateway or hybrid model when - You need multi-provider routing, request-level policy, custom middleware, or real-time blocking beyond IAM. - CLI, desktop, and different teams need to share the same identity, cost, and support model. - The extra layer has a clear operational owner; otherwise, it becomes another failure point. ### From first test to team use 1. **Week 0-1: decision and access** — Map AWS Organization, accounts, SCPs, IAM Identity Center, profiles, Regions, quotas, models, first-use requirements, owners, and the starting setup: CLI, Desktop/Cowork with third-party inference, or hybrid. 2. **Week 2: controlled pilot** — Test real tasks in chosen repositories with human review, temporary credentials, pinned models, CloudTrail, OpenTelemetry, budgets, and a runbook for model, network, quota, or authentication failures. 3. **Week 3+: expansion with clear criteria** — Expand only when security, platform, and engineering approve metrics, feedback, cost per user, failure rate, repository boundaries, secret handling, and support. ### What breaks when more people start using it The first install may work. Problems start when these issues reach the pilot too late. #### Access and availability - The Claude model is not enabled, first-use steps are incomplete, or the chosen model does not appear in the expected AWS Region. - Inference profiles use another AWS Region and SCPs block the required routing. - Streaming quotas, latency, or throughput have not been tested with real tasks. #### Credentials and identity - AWS_REGION is not explicit, the AWS profile expires without a clear refresh path, or IAM policy becomes too broad to speed up the pilot. - Persistent API keys enter the pilot without MFA, user attribution, and protection against repository commits. - The team chooses quick SSO, then later needs per-user activity and cost data the initial integration does not provide. #### Code, data, and operations - Sensitive repositories enter too early without rules for secrets, customer data, destructive commands, or human approval. - Logs, prompts, source code, and activity data are discussed only after security is already blocking expansion. - There is no support path for model, authentication, network, quota, MDM, or unexpected cost failures. ### What Elevata helps you decide - **Architecture decision:** Recommendation across Claude Code CLI, Claude Desktop/Cowork with Bedrock-backed inference, a hybrid model, Anthropic SaaS, or a gateway. - **Risk register and blockers:** Prioritized list of what needs fixing across IAM, SCPs, models, Regions, quotas, credentials, MDM, logs, repositories, secrets, and support. - **Cost and observability model:** Plan for CloudTrail, OpenTelemetry, CloudWatch, budgets, alerts, cost by user/team, and adoption metrics before expansion. - **Pilot action plan:** Actions for SSO/IdP, MDM, IaC, onboarding scripts, policies, support, and criteria for deciding the next step. ### Guides for planning Claude on Bedrock - **Technical Claude Code on AWS guide:** Read the technical walkthrough for IAM, variables, models, third-party inference, self-hosting, and troubleshooting. (/en/claude-code-on-aws-complete-guide-bedrock-setup-self-hosted-models) - **Claude Cowork on Amazon Bedrock:** Pilot Cowork and the Code tab with MDM, workspace rules, observability, and clear controls. (/en/solutions/claude-cowork-amazon-bedrock) - **Amazon Bedrock Consulting:** Bedrock architecture for RAG, agents, guardrails, evaluation, and operations. (/en/amazon-bedrock-consulting) ### Technical sources - Claude Code on Amazon Bedrock: https://docs.anthropic.com/en/docs/claude-code/amazon-bedrock - Claude Cowork on 3P: https://claude.com/docs/cowork/3p/overview - Claude on AWS: https://claude.com/partners/claude-on-aws - AWS best practices for deploying Claude Code with Bedrock: https://aws.amazon.com/blogs/machine-learning/claude-code-deployment-patterns-and-best-practices-with-amazon-bedrock/ - Amazon Bedrock model access: https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html - Amazon Bedrock models at a glance: https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards.html - Amazon Bedrock inference profiles: https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html ### FAQ **What requirements need to be ready for Claude Code on Bedrock?** At minimum, the team needs an AWS account with Bedrock enabled, an available Claude model, explicit AWS_REGION, AWS credentials, IAM to invoke models, enough quota, pinned models, and controls for logs, cost, human review, allowed repositories, and support. **Is Claude Code on Amazon Bedrock the same as Claude Team or Enterprise?** No. Bedrock is an AWS-managed inference path for models. Claude Team or Enterprise provides Anthropic's managed SaaS experience. Some companies use both, but identity, cost, data, and administration are different. **Can Claude Desktop/Cowork with third-party inference use Bedrock?** Yes, when configured with inferenceProvider=bedrock. Because this path is in Research Preview, validate availability, MDM, credentials, egress, local files, plugins, MCP, observability, and the Code tab separately from the CLI path for any team pilot. **How long does a Claude Code with Bedrock pilot take?** A small pilot can start in days when the account, IAM, model, and pilot group already exist. Expanding safely usually means aligning MDM, policies, observability, security, support, training, metrics, and expansion criteria. **Does running Claude Code through Bedrock automatically reduce data risk?** It can reduce some risks when the company needs AWS-controlled model access, billing, identity, logs, and Regions. But Bedrock does not make the rollout safe by itself. Risk depends on allowed repositories, credentials, logs, secrets, commands, human review, and data-flow approval. **Should we use API keys, SSO, or direct IdP integration?** API keys are fast for proof of concept but weak for production because MFA, user attribution, rotation, and leak control are harder. SSO with IAM Identity Center works well for controlled pilots. Direct IdP integration usually fits when the company needs detailed attribution, observability, and controls for a larger rollout. **How do we control cost and adoption?** Set a budget before the first pilot, track CloudTrail and CloudWatch, configure OpenTelemetry when development-activity metrics are needed, and attribute cost by user, team, repository, or inference profile. Expansion should depend on metrics, not only pilot enthusiasm. **What should we prepare before talking to Elevata?** Before we talk, a short summary helps: AWS structure, accounts, identity model, pilot group, Claude tools, security constraints, data residency or privacy requirements, candidate repositories, and current stage. Do not send keys, tokens, passwords, MFA codes, secrets, source code, or customer data. --- ## Claude on Amazon Bedrock for Canadian Companies URL: https://elevata.io/en/claude-on-amazon-bedrock-canada Elevata helps Canadian companies assess, architect, and operate Anthropic Claude use cases on Bedrock, including RAG, guardrails, privacy, costs, and Region decisions. - **Model:** Anthropic Claude via Bedrock - **Market:** Canada - **Architecture:** RAG, Guardrails, CRIS - **Residency:** by design ### When does Claude on Bedrock fit? Claude on Bedrock fits when a company wants advanced models inside an AWS architecture with identity, network, logging, privacy, RAG, and governance controls. The focus should be the use case: support, search, document analysis, internal copilots, or process automation. ### How should teams think about Region and privacy? For Canadian companies, we assess model availability, Canada Central usage, cross-Region inference, retention policies, logs, and data sent to the model. The architecture should document which data moves, where it resides, and which controls reduce risk. ### Before putting Claude on Bedrock into production The decision is not just choosing Claude. The architecture needs to make data, Region, logs, cost, and fallback explicit. #### Bedrock or direct API - Use Claude through Bedrock when AWS governance, billing, IAM, networking, logging, procurement, and enterprise controls matter. - Consider direct API only when a required capability is unavailable through Bedrock and security/procurement approve that path. - Document model, fallback, invocation path, and owner before production. #### Region and data questions - Is the target model available in the planned Region, or will cross-Region inference profiles (CRIS) be required? - Do prompts, documents, logs, or responses include personal, financial, health, contract, or intellectual-property data? - Which data can leave Canada, which needs masking, and which should never reach the model? #### Implementation options - Simple prompt app for controlled, lower-risk tasks. - RAG with Knowledge Bases or a custom vector stack when answers depend on enterprise documents. - Agent with tools when AI needs to query APIs, create tickets, or follow approved workflows. #### Production criteria - Evaluation set, safe logging, user limits, cost monitoring, and model rollback. - Guardrails for sensitive data, unsafe answers, prompt injection, and tool calling. - Decision documentation: Region, model, data sent, retention, and workflow owner. ### How to decide Claude, Region, and controls in Canada #### Decision matrix - Use Bedrock access in the approved Region when the model and required features are available and residency is strict. - Use an approved inference profile when capacity or model access requires it and legal, security, and architecture accept the routing. - Use a non-Canada Region only when data classification, contracts, and customer commitments explicitly allow it. #### Minimum production architecture - Application in the approved Region, scoped IAM roles, Guardrails where appropriate, and logs without sensitive content by default. - Sensitive documents in S3 with explicit policies; encrypted indexes; observability separating metadata from prompts. - Decision record with target model, invocation path, fallback, data sent, retention, and owners. ### What we assess before production - **Model and task selection:** We compare quality, latency, cost, and security for each workflow before making Claude the default. - **RAG with enterprise data:** We design ingestion, chunking, permissions, filters, and evaluation for answers grounded in controlled data. - **Security, privacy, and guardrails:** We map sensitive data, logs, network, IAM, Guardrails, and approval criteria to reduce operating risk. - **Cost and observability:** We measure cost per task, environment limits, and alerts to avoid unexpected production spikes. ### Continue through Bedrock architecture - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Brazil:** Bedrock architecture with attention to LGPD, logs, and the São Paulo Region. (/en/amazon-bedrock-consulting-brazil) - **Amazon Bedrock Cost Optimization:** Control model, prompt, and RAG cost on Bedrock. (/en/amazon-bedrock-cost-optimization) - **Claude on Bedrock for Canada:** Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads. (/en/claude-on-amazon-bedrock-canada) ### Technical sources - Claude on AWS: https://claude.com/partners/claude-on-aws - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html - Bedrock data protection: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html ### FAQ **Can Canadian companies use Claude on Bedrock?** Yes, subject to model availability, Region, and AWS account configuration. The design should assess data requirements, logs, cross-Region inference profiles (CRIS), and internal controls before production. **Does Claude on Bedrock use my data to train models?** AWS documents data-protection controls for Amazon Bedrock. For risk decisions, validate the current AWS documentation, applicable terms, and internal privacy requirements. **Do I need RAG to use Claude?** Not always. RAG is recommended when answers need to use enterprise data, policies, documents, or current context that is not part of the base model. --- ## RAG and MCP on AWS for Canadian Companies URL: https://elevata.io/en/rag-and-mcp-on-aws-for-canadian-companies Elevata designs AI systems that combine knowledge retrieval, Model Context Protocol (MCP), tool context, and AWS services to deliver auditable answers and actions. ### Architecture decisions before you build - **Retrieved, embedded, and logged data:** Which documents, records, prompts, embeddings, logs, traces, and evaluation data can be used by the AI workflow, and where are they allowed to live? - **Knowledge Bases, vector stores, search, and ranking:** Will the system use Bedrock Knowledge Bases, a custom vector store, search APIs, or a hybrid approach? How will permissions, freshness, citations, and source ranking be handled? - **Explicit, limited, and reversible actions:** Which actions are read-only, which require approval, and which should never be delegated to an agent? MCP should make tool access explicit, limited, logged, and reversible. - **Quality, cost, latency, and auditability:** What will be measured before launch: answer quality, retrieval accuracy, tool-call accuracy, cost per answer, latency, safe refusal, auditability, and rollback? - **Region and privacy by workflow:** Decide whether prompts, retrieved context, embeddings, logs, traces, backups, and evaluation data need to stay in a Canadian AWS Region, and whether cross-Region inference is acceptable for the workflow. ### RAG connects models to controlled knowledge RAG retrieves documents, policies, data, or search results to ground model answers. In production, the challenge is permissioning, freshness, chunking, evaluation, traceability, and cost. ### MCP organizes tool context Model Context Protocol (MCP) helps agents and applications communicate with tools and data sources in a standardized way. On AWS, it needs to integrate with IAM, network, logs, secrets, limits, and observability. ### When to use RAG, MCP, and agents RAG and MCP solve different problems. The architecture should decide when to answer, when to retrieve context, and when to act. #### Use RAG when - The answer needs policies, documents, contracts, tickets, records, or current knowledge. - You need citations, permission-aware retrieval, and traceability for the context behind the answer. - The goal is better answers, not executing actions in external systems. #### Add MCP when - AI needs access to tools, APIs, databases, CRMs, tickets, or sources that change often. - You want to standardize tool context across multiple agents or applications. - Actions need authentication, limits, approval, logging, and user-level isolation. #### Failure modes to avoid - Permission leakage: a user gets an answer grounded in a document they should not access. - Over-retrieval: too much context increases cost, noise, and answer risk. - Tool calling without approval: an agent executes a real action without limits, audit, or human confirmation. ### How RAG and MCP connect in production #### Reference flow - User -> authentication and role context -> router -> RAG retriever -> knowledge base/vector index -> MCP server for approved tools. - Bedrock receives minimal context, Guardrails and application policy validate answers and tool calls, and human approval enters when the action is sensitive. - Logs, traces, and the evaluation set capture metadata, sources, permissions, and cost without storing sensitive prompts by default. #### Canadian design questions - Do retrieved context, embeddings, logs, traces, backups, or evaluation data need to stay in a Canadian Region? - Is CRIS or another cross-Region route acceptable for this workflow, contract, or customer policy? - Which privacy, vendor-risk, or document-permission review needs to happen before the pilot? #### When MCP is not needed - Static FAQ, documentation search, or simple chatbot with no actions in external systems. - Deterministic workflow where forms, rules, and traditional integrations are clearer than an agent. - Scenario with no owner to approve tool calls, limits, audit, and failure states. ### What should a RAG + MCP architecture cover? - **Ingestion and permissions:** We map sources, freshness, access filters, and traceability by user or role. - **RAG and evaluation:** We define chunking, embeddings, retrieval, quality tests, and workflow-level metrics. - **MCP and tools:** We connect tools with limits, authentication, logs, and approval to reduce the risk of wrong actions. - **Operations and cost:** We create observability, budgets, fallback, alerts, and playbooks for production operations. ### See RAG and MCP in real context - **Claude Code on Amazon Bedrock:** Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering. (/en/claude-code-on-bedrock) - **Amazon Bedrock Consulting in Canada:** RAG, agents, and Region decisions for Canadian teams. (/en/amazon-bedrock-consulting-canada) - **Amazon Bedrock Consulting in Brazil:** Bedrock architecture with attention to LGPD, logs, and the São Paulo Region. (/en/amazon-bedrock-consulting-brazil) - **Amazon Bedrock Cost Optimization:** Control model, prompt, and RAG cost on Bedrock. (/en/amazon-bedrock-cost-optimization) - **Claude on Bedrock for Canada:** Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads. (/en/claude-on-amazon-bedrock-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - AWS Well-Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Amazon Bedrock Agents: https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html - Model Context Protocol: https://modelcontextprotocol.io/ ### FAQ **What is the difference between RAG and MCP?** RAG retrieves knowledge to ground answers. Model Context Protocol (MCP) standardizes how applications and agents access tools and context sources. They are complementary in AI systems that need to answer and act. **Can RAG and MCP run on AWS in Canada?** Yes, depending on chosen services, Region requirements, and availability. The design should assess data, logs, network, authentication, and integrations before production. **When should we use agents instead of only RAG?** Use agents when AI needs to execute steps, query tools, call APIs, or follow workflows. For informational answers only, simple RAG may be enough. --- ## RAG and MCP on AWS for Brazilian Companies URL: https://elevata.io/en/rag-and-mcp-on-aws-for-brazilian-companies Elevata designs AI systems in Brazil that combine knowledge retrieval, Model Context Protocol (MCP), tool context, and AWS services with attention to data, logs, the São Paulo Region, and operations. ### Architecture decisions before you build - **Retrieved, embedded, and logged data:** Which documents, records, prompts, embeddings, logs, traces, and evaluation data can be used by the AI workflow, and where are they allowed to live? - **Permissions, freshness, and citations:** Will the system use Bedrock Knowledge Bases, a custom vector store, search APIs, or a hybrid approach? How will permissions, freshness, citations, chunking, and source ranking be handled? - **Actions with approval, limits, and rollback:** Which actions are read-only, which require approval, and which should never be delegated to an agent? MCP should make tool access explicit, limited, logged, and reversible. - **Region, LGPD, and Portuguese-language quality:** Decide whether prompts, documents, embeddings, logs, traces, backups, and evaluation sets should stay in Brazil, and test Portuguese-language quality for legal, financial, operational, and customer-support terminology. ### RAG connects models to controlled knowledge RAG retrieves documents, policies, data, or search results to ground model answers. In production, the challenge is permissioning, freshness, chunking, evaluation, traceability, and cost. ### MCP organizes tool context Model Context Protocol (MCP) helps agents and applications communicate with tools and data sources in a standardized way. On AWS, it needs to integrate with IAM, network, logs, secrets, limits, observability, and Brazil privacy requirements. ### When to use RAG, MCP, and agents in Brazil The design needs to respect permissions, Portuguese-language quality, LGPD, traceability, and limits for actions in real systems. #### When RAG is enough - Internal policies, product docs, support knowledge, contracts, manuals, legal, financial, or HR process documents. - The goal is answering with correct sources and permissions, not executing actions in external systems. - Document owners can keep content, validity, classification, and permissions current. #### When to add MCP - AI needs to query order status, payments, tickets, CRM, internal APIs, or multi-step workflows. - Tools and sources change frequently and need standardized contracts across multiple agents. - Actions require authentication, limits, approval, logs, and user-level isolation before execution. #### Brazil design questions - Do prompts, documents, embeddings, traces, backups, or logs contain personal, financial, health, customer, or employee data? - Where do vector indexes, Knowledge Bases, logs, traces, and evaluation sets live, and who approves that decision? - How will Portuguese quality be evaluated: legal/financial terms, accents, OCR, abbreviations, tone, citations, and refusal behavior? ### RAG + MCP on AWS with Brazil-specific controls #### Reference flow - User -> authentication and role -> router -> RAG retrieval -> vector index or Knowledge Base -> MCP server -> Bedrock. - Guardrails and application policy validate answers, tool calls, sensitive data, and human approval when required. - Logs, traces, evaluation, and audit capture operational metadata without exposing sensitive prompts by default. #### Portuguese-language evaluation - Test domain terms, mixed PT/EN documents, OCR quality, abbreviations, accents, and answer tone. - Validate citations, source retrieval, safe refusal, missing-data answers, and tool calls in failure states. - Do not use RAG to bypass document governance; stale, duplicate, or poorly permissioned content needs to be fixed at the source. ### What the architecture includes - **Ingestion and permissions:** We map sources, freshness, access filters, and traceability by user or role, with attention to sensitive data and privacy requirements. - **RAG and evaluation:** We define chunking, embeddings, retrieval, quality tests, and workflow-level metrics before taking the use case to production. - **MCP and tools:** We connect tools with limits, authentication, logs, and approval to reduce the risk of wrong actions in real workflows. - **Brazil operations:** We review Region, logs, cost, observability, and controls that support Portuguese-language operations and Brazilian requirements. ### Continue through RAG, MCP, and AWS GenAI - **RAG + MCP in Canada:** See the same architecture pattern with Canada-specific Region and operations questions. (/en/rag-and-mcp-on-aws-for-canadian-companies) - **Amazon Bedrock in Brazil:** RAG, agents, Guardrails, and Region decisions for Brazilian workloads. (/en/amazon-bedrock-consulting-brazil) - **AI inference cost optimization:** Measure cost per answer, ticket, document, or automation before scaling usage. (/en/aws-ai-inference-cost-optimization) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html - Amazon SageMaker AI: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html - Bedrock security and privacy: https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - Amazon Bedrock model support by Region: https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html - Amazon Bedrock cross-Region inference: https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html - Knowledge Bases for Amazon Bedrock: https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html - Amazon Bedrock Agents: https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html - Model Context Protocol: https://modelcontextprotocol.io/ ### FAQ **What is the difference between RAG and MCP?** RAG retrieves knowledge to ground answers. Model Context Protocol (MCP) standardizes how applications and agents access tools and context sources. They are complementary in AI systems that need to answer and act. **Can RAG and MCP run on AWS in Brazil?** Yes, depending on chosen services, Region requirements, and availability. The design should assess data, logs, network, authentication, integrations, and when the São Paulo Region makes sense. **Can RAG and MCP help with LGPD requirements?** They do not replace legal governance, but the architecture can support access controls, data minimization, logs, traceability, and residency when the workload requires it. The analysis should be use-case specific. --- ## Stop rationing observability. URL: https://elevata.io/en/coralogix-observability Replace high-cost or fragmented observability tooling with Coralogix. Elevata maps the dashboards, alerts, SLOs, integrations, retention rules, and response workflows your operations depend on, validates them in parallel, and leads the production cutover. ### From the current bill to production cutover 1. **Baseline current cost and usage** — Record telemetry sources, volumes, cost, retention, dashboards, alerts, SLOs, and integrations. 2. **Compare coverage and projected cost** — Map each requirement to Coralogix, identify gaps, and project cost from actual consumption. 3. **Validate representative workloads** — Instrument a representative workload and compare critical workflows across both platforms. 4. **Migrate production workflows** — Migrate pipelines, dashboards, alerts, queries, integrations, retention, and runbooks. Then prepare the teams that will use the new environment. 5. **Cut over and stabilize** — Complete production checks, make the controlled switch, and retire redundant tooling only after the new environment is stable. ### Check Coralogix against your requirements - **Log analytics:** Search, correlate, and analyze logs at scale. - **Metrics and infrastructure monitoring:** Visibility across services, hosts, cloud resources, and dependencies. - **Distributed tracing and APM:** Follow transactions, latency, and failures across services. - **Real user monitoring and session replay:** Connect real user experience to backend behavior. - **Dashboards, alerts, SLOs, and incident workflows:** Reproduce the operational workflows that guide response and reliability. - **Kubernetes, containers, and serverless:** Observe clusters, workloads, functions, and modern platforms. - **OpenTelemetry and cloud integrations:** Collect logs, metrics, and traces through open standards and cloud integrations. - **SIEM and security analytics:** Correlate operational and security signals in the same platform. - **AI and LLM observability:** Monitor AI applications, LLM traces, evaluations, costs, and guardrails. ### AWS cost, modernization, and ongoing operations - **AWS cost optimization:** Combine observability economics with a FinOps view of the AWS environment. (/en/aws-cost-optimization) - **Cloud modernization:** Build telemetry into platform, Kubernetes, and serverless modernization. (/en/solutions/cloud-modernization) - **Elevata Orbit:** Continue optimization with senior AWS engineering on demand. (/en/solutions/elevata-orbit) ### FAQ **Can Coralogix replace Datadog or Sumo Logic?** It can, provided it covers the workflows your operations depend on. Coralogix supports logs, metrics, traces, APM, infrastructure monitoring, RUM, security, and AI observability. Elevata compares those capabilities with your current environment and validates the critical workflows before cutover. **How much can we save?** Savings depend on telemetry volume, current pricing, retention, indexing, query patterns, and the capabilities in use. Elevata compares your bill and consumption with the projected Coralogix cost. In Coralogix's published case study, Curve reported a 40% reduction after moving from Datadog; that result is not a guarantee for other environments. **Do we need to migrate everything at once?** No. The recommended approach starts with representative workloads and parallel validation. Migration can then proceed by workload, telemetry type, team, or environment. **Can Elevata migrate our dashboards and alerts?** Yes. The migration can include telemetry pipelines, dashboards, alerts, SLOs, queries, integrations, retention policies, runbooks, and team training. **Does Coralogix support OpenTelemetry?** Yes. Coralogix supports OpenTelemetry for logs, metrics, and traces, alongside AWS, Azure, GCP, Prometheus, Fluent Bit, and other common telemetry sources. **Where is the data stored?** Storage depends on telemetry priority. High-priority data can use Coralogix hot storage, while monitoring and compliance data can be retained in customer-controlled object storage and queried remotely. Elevata designs the retention and routing model around performance, compliance, and cost requirements. **Can Coralogix be purchased through AWS Marketplace?** Yes. Coralogix is available through AWS Marketplace. Elevata can include Marketplace procurement in the technical and commercial migration plan. **Does this include AI and LLM observability?** Yes. Coralogix includes AI observability capabilities such as application visibility, LLM tracing, evaluation, cost tracking, guardrails, and security posture. Elevata can integrate these capabilities into production AI and Amazon Bedrock architectures. --- ## AWS Consulting across Canada and Brazil URL: https://elevata.io/en/aws-consulting-canada-brazil Elevata helps Canadian companies operating in Brazil, Brazilian companies entering Canada, and teams that need to work across both markets when they need practical AWS guidance for migration, modernization, data, generative AI, security, and operations. The work combines coordination across Toronto, Brazil, English, and Portuguese to reduce friction between business, engineering, and compliance. - **Market:** Canada and Brazil - **Focus:** AWS Consulting - **AWS services:** Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations - **Residency and compliance:** Workload-specific ### When does this engagement make sense? AWS Consulting across Canada and Brazil fits when the company already has delivery pressure but needs to reduce technical risk before scaling. Canada-Brazil operations need to avoid country-by-country silos: data, identity, cost, support, and ownership need to work on both sides. The starting point is separating reversible decisions from structural ones: Region, data, identity, network, cost, integration, and operations. That keeps the roadmap executable by engineering squads, not just slideware. ### How does delivery work across Canada and Brazil? Delivery combines architecture workshops, technical assessment, implementation planning, and execution with AWS specialists. For Canadian and Brazilian teams, we account for language, time zone, stakeholder access, privacy requirements, and Region design from the start. Multi-Region architectures can separate workloads, data, and controls by country when privacy, latency, or continuity requirements call for that split. ### Practical choices for Canada-Brazil operations The value is avoiding duplicated platforms, unclear ownership, and inconsistent data controls across countries. #### Common scenarios - Canadian company with product, support, engineering, or finance operations in Brazil. - Brazilian company entering Canada and needing AWS, security, and bilingual documentation alignment. - Product serving users in both countries, with latency, data, support, and costs that need separate measurement. #### Design decisions - One AWS Organization with country-specific accounts versus separate organizations for local governance. - Shared data versus country-specific zones in the data lake, with clear access and retention policies. - Centralized security versus local operations: who approves changes, incidents, access, and exceptions. #### What usually goes wrong - Logs and backups crossing borders without an explicit risk and retention decision. - Two teams creating different standards for IAM, tags, network, CI/CD, and cost. - Documentation in only one language, weakening handoff, audit, and local-hours support. ### Two common models for operating across Canada and Brazil The goal is not duplicating the platform by country. It is deciding consciously where to separate, where to share, and who operates each layer. #### Pattern A: one AWS Organization with country-specific accounts Works when there is a shared product or platform, a common security team, and a need to allocate cost by country. - Central security/logging account, country-specific production accounts, and data zones with clear retention policies. - IAM Identity Center or common federation, with groups, break-glass access, and bilingual runbooks. #### Pattern B: separate Organizations Fits when legal entities, finance, audit, or governance requirements are strongly separated. - Cross-country integration through APIs, queues, controlled replication, and clear data contracts. - Minimum alignment on tags, naming, incidents, and controls to avoid two incompatible platforms. ### What the engagement includes - **Assessment and architecture:** We map goals, workloads, data, integrations, and local constraints to define a viable AWS architecture for Canada and Brazil. - **Technical proof with a production path:** Prototypes are treated as the start of the product: logs, security, cost, rollback, IaC, and operating criteria come in early. - **Governance, security, and cost:** We define identity, data, observability, tags, budgets, and FinOps decisions before expanding usage or traffic. - **Execution with a senior team:** Elevata works from strategy through implementation, with AWS specialists who can discuss architecture and also deliver code, infrastructure, and operations. ### See proof across both markets - **Canadian travel AI search:** Bedrock, MCP, RAG, and search ranking in an AWS architecture for a Canadian travel company. (/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws) - **Azure-to-AWS migration proof:** See how an AWS migration reduced cost and increased operational control for a Brazilian business. (/en/case-studies/credaluga-azure-to-aws-migration-with-cost-reduction) - **AWS Consulting in Brazil:** AWS architecture with Brazilian context, LGPD, the São Paulo Region, and bilingual delivery. (/en/aws-consulting-brazil) - **Amazon Bedrock in Canada:** RAG, agents, guardrails, and Region decisions for Canadian workloads. (/en/amazon-bedrock-consulting-canada) ### Technical sources - Elevata official AWS Partner Network profile: https://partners.amazonaws.com/partners/0018W00002YlROlQAN/Elevata - AWS Migration Acceleration Program: https://aws.amazon.com/migration-acceleration-program/ - AWS Well-Architected: https://aws.amazon.com/architecture/well-architected/ - AWS data privacy in Brazil: https://aws.amazon.com/compliance/brazil-data-privacy/ - AWS Global Infrastructure: https://aws.amazon.com/about-aws/global-infrastructure/ - AWS shared responsibility model: https://aws.amazon.com/compliance/shared-responsibility-model/ - AWS Regions, including Canada Central and Canada West: https://docs.aws.amazon.com/global-infrastructure/latest/regions/aws-regions.html - AWS Migration Acceleration Program (MAP): https://aws.amazon.com/migration-acceleration-program - PIPEDA, Canada's federal private-sector privacy law: https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/ - Quebec private-sector privacy law: https://www.legisquebec.gouv.qc.ca/en/document/cs/P-39.1 ### FAQ **Does AWS Consulting across Canada and Brazil require local presence?** Not always, but proximity helps when there are executive workshops, architecture decisions, privacy requirements, or distributed teams. For Canada and Brazil, Elevata combines local presence when needed with senior remote delivery. **Which services are part of AWS Consulting across Canada and Brazil?** A typical scope includes Migration and MAP, Cloud-native modernization, Data and generative AI, FinOps and operations. The final selection depends on the workload, available data, security requirements, operations, and cost. **Can AWS Consulting across Canada and Brazil help with data residency?** Yes, when data residency is a workload requirement. The analysis defines which data needs to stay in which Regions, how logs and backups are handled, and which integrations may cross borders. Multi-Region architectures can separate workloads, data, and controls by country when privacy, latency, or continuity requirements call for that split. The final decision should be validated against your internal requirements and legal review. **How long does it take to start?** The starting point is a scoped assessment, followed by an implementation plan sized to the first priority workload or use case. --- # How do you pronounce Elevata? URL: https://elevata.io/en/how-do-you-pronounce-elevata Hear the pronunciation, see the syllable breakdown, and learn how to say Elevata in Portuguese and English. ## What is the correct way to say Elevata? The simplest way is eh-leh-VAH-tah: four syllables, with the stress on VAH. End on tah, not tay. ## What does Elevata mean? Elevata comes from elevar, the Portuguese verb meaning to lift or raise something to a higher level. ## How do you keep it straight? - **It isn't “elevator”:** The ending is -vata (VAH-tah), not -vator. - **Stress falls on VAH:** The third syllable carries the name; eh and leh stay quick and light. - **Think elevate:** Same idea as the verb: raising the level. ## Common questions about Elevata **How do you pronounce Elevata?** Elevata is pronounced eh-leh-VAH-tah, with the stress on VAH. **What does Elevata mean?** Elevata comes from elevar in Portuguese: to elevate, raise, or bring something to a higher level. It also reflects the company's Brazilian roots, from its start in São Paulo to its expansion into North America. **Is Elevata a Portuguese word?** Elevata is a brand name built from Portuguese. The core idea comes from elevar, which means to elevate. ## Language references Sources that confirm the meaning of elevar and elevate. - Cambridge Dictionary: elevate in Portuguese: https://dictionary.cambridge.org/dictionary/english-portuguese/elevate - Collins Dictionary: Portuguese elevar: https://www.collinsdictionary.com/dictionary/portuguese-english/elevar - Merriam-Webster: elevate: https://www.merriam-webster.com/dictionary/elevate --- # Case Studies ## Brazilian Venture Capital: AI Assistant on AWS Accelerates Decisions URL: https://elevata.io/en/case-studies/assistente-interno-ia-aws-venture-capital ## About the Company A venture capital fund whose investment team depends on fast access to high-quality internal context — deal history, partner discussions, meeting notes, and ongoing portfolio updates — to make decisions and advance opportunities with agility. As with most VC teams, the fund's operations rely on multiple tools such as chat, documents, and CRM. The challenge was not the lack of information, but its excess, distributed across different systems, difficult to retrieve quickly, and complicated to validate under pressure — especially considering that access to sensitive deal data must be rigorously controlled. ## The Challenges The investment team needed a reliable way to search and synthesize internal knowledge without switching between tools or relying on informal "ask someone" workflows. Critical information was scattered across Slack threads, Notion pages, Google Drive documents, and Affinity CRM records. This fragmentation made it difficult to build a single source of truth for deal history and references, while adding latency to day-to-day decision-making. Before the project, finding the right context to answer a single question took an average of 8 to 10 minutes. The time spent was only part of the problem. Access control was equally critical: the fund needed a solution capable of delivering fast answers without violating permissions, ensuring that information retrieval and responses were always based solely on documents each user was authorized to view, in compliance with security and privacy policies. ## The Solution Elevata designed and implemented a GenAI-based knowledge assistant, operating directly within Slack and capable of retrieving reliable, permission-aware context from the fund's internal systems. Rather than training a model on the fund's data, the solution was built with a Retrieval Augmented Generation (RAG) approach. In this model, the assistant retrieves relevant internal content at query time and uses that context to generate the response, keeping data under control and preventing unauthorized exposure. ### **Architecture decisions and model selection** Elevata began the project by evaluating different foundation models available via Amazon Bedrock and SageMaker JumpStart. The goal was not only generation quality but also deployment security and compatibility with a RAG architecture capable of applying access control at data retrieval time. Evaluation criteria included language fluency and summarization quality, ability to anchor responses in corporate knowledge bases, ease of integration with the fund's stack, and support for scalable vector search with document-level access control. The final architecture combined: - Amazon Bedrock for response generation - Amazon OpenSearch Service for vector search and data retrieval - Role-based access control (RBAC) in OpenSearch, applied during retrieval, ensuring results are filtered by user before reaching the model - Embeddings generated with Cohere, stored in k-NN indices in OpenSearch to support real-time semantic search This architecture ensured high-quality responses, always anchored in trusted internal sources and aligned with the fund's permission model. ### **Building the knowledge layer via retrieval (not training)** Rather than fine-tuning an LLM, Elevata structured the fund's knowledge as an indexed dataset and used retrieval to provide runtime context. Indexed content included meeting notes, investment memos, Slack threads, and investor updates. Documents were enriched with metadata — such as author, tags, teams, creation date, and permissions — enabling retrieval to be filtered precisely, not only by semantic similarity but also by scope and access. ### **Data ingestion and platform integrations** To connect the fund's systems into a usable knowledge base, Elevata implemented ingestion pipelines using Airbyte and AWS Lambda. These pipelines collected data from: - Slack (channels and threads) - Notion (pages and blocks) - Google Drive (documents and spreadsheets) - Affinity CRM (deals and pipeline context) Each source was normalized and indexed in OpenSearch, with metadata preserving origin and improving filtering during retrieval. ### **Retrieval and response flow in Slack** At runtime, the assistant follows a clear, permission-aware flow: - The user asks a natural language question in Slack. - The assistant authenticates the user via AWS Cognito (JWT-based authentication). - The system performs a semantic vector search in OpenSearch, applying RBAC filters to ensure only authorized documents are retrieved. - The selected passages are sent to a model on Amazon Bedrock, which generates a coherent response personalized to the user's question and based exclusively on retrieved internal context. This flow allowed the team to ask a single question and receive a response combining information from multiple sources — for example, synthesizing a Slack discussion, an investment memo in Notion, and the latest deal status in Affinity — without switching between tools or manually assembling the narrative. ### **Iteration and testing** Elevata conducted two rounds of acceptance testing with the fund's partners and analysts. Tests focused on accuracy, latency, and edge case handling. After each round, prompt templates and retrieval filters were refined to increase first-response resolution rate and reduce false positives in content retrieval. ## The Results The assistant significantly reduced the operational friction associated with the fund's internal knowledge work, centralizing search and synthesis in a single Slack experience without compromising rigorous per-user access controls. After deployment, the fund observed measurable gains: - approximately 80% reduction in time spent searching for context, accelerating daily investment operations - Average latency below 2 seconds per query, enabling rapid iteration in Slack conversations - 93% understanding accuracy and 88% response coherence, according to internal evaluation - 85% first-response resolution, reducing the need for additional searches - Over 90% weekly usage by the team within three weeks, indicating high adoption among partners and analysts Beyond speed and usability, the solution strengthened the internal documentation culture by improving traceability. Since the assistant always retrieves information from metadata-tagged indexed sources, users can trace responses directly back to originating documents and discussions. --- ## Agentic AI: From Per-Token Pricing to Scalable Inference on AWS URL: https://elevata.io/en/case-studies/case-inference-escalavel-aws ## About the Company A Brazilian Agentic AI company focused on developing resilient, secure, and culturally aware AI agents capable of creating more humanized interactions between people and systems. Offered as AI-as-a-Service (AIaaS), its solutions enable organizations to deploy "digital people" on the front lines of customer service, operating at scale, integrated with existing software stacks, and collaborating with human teams. The company serves large organizations, including financial sector institutions and environments with millions of users. In this context, customer experience systems must operate consistently under high volume while meeting strict security, governance, and legal accountability requirements. As a result, cost predictability and operational control are as critical as model quality. ## The Challenges The platform was using Amazon Bedrock, but the token-based inference economics began creating a ceiling for growth at scale. High token consumption generated elevated variable costs, and this variability made planning and sustainable operational expansion difficult. Additionally, at high volumes, constraints associated with managed services — such as throughput limits and performance control — become more visible, especially when predictable latency and high responsiveness are fundamental to the user experience. In summary, the need was to control inference costs without compromising quality. A model was also needed that would allow scaling the user base without costs growing linearly with token usage, plus greater control over where inference is executed and where data resides — a particularly sensitive point for enterprise clients and regulated environments. ## The Solution Elevata conducted a structured benchmarking and migration program to move operations from a managed, token-based inference model to a capacity-based inference model, using open-weight models hosted within the client's own AWS environment. The work began with an inference benchmarking phase across different hardware options and instance families. Elevata tested open-weight models on chips and accelerators including AWS Inferentia2 and GPUs from the L40S, H100, and B200 Blackwell families. Hardware selection was not treated as merely an infrastructure decision: tests were directly tied to operational inference KPIs such as tokens per second, latency, output characteristics, and functional validation of candidate model quality. This phase generated an objective decision basis, identifying which combinations offered the best balance of cost, throughput, and response time without compromising the client's functional requirements. With this baseline defined, Elevata implemented the migration from Bedrock usage to self-hosted open-weight models on AWS instances with dedicated inference GPUs and chips. This shift fundamentally changed the financial model of the operation. Instead of paying a variable rate per token, the client now pays for infrastructure capacity. This capacity can be planned, measured, and scaled predictably, providing greater clarity on unit economics and reducing vendor-imposed limitations that tend to appear in high-volume scenarios. Additionally, the client can now support more users on the same infrastructure base and scale capacity for processing peaks without needing to restructure the application layer. Security posture was treated as a core requirement from the start. The new inference stack was deployed within the client's VPC, keeping inference execution and data handling entirely within the company's AWS environment. For enterprise and financial sector clients, this means greater control over data boundaries, alignment with compliance requirements, and more direct governance over infrastructure configuration. ## The Results By replacing managed token-based inference with capacity-based inference using open-weight models hosted on AWS, the client reduced inference costs by 35%. More important than the percentage reduction was the change in the economic "shape" of the operation. Instead of costs growing directly with token volume, the client can now plan inference spending based on capacity and scaling patterns, making platform growth much more predictable. Performance also improved, with lower and more consistent latency, enabled by greater infrastructure-level control and the removal of external throughput limitations. For enterprise clients, the new model strengthened security and compliance posture by running inference within the company's own VPC, expanding control over data locality and operational governance. The roadmap builds on this same foundation: the client and Elevata plan to test agent optimization techniques ("agent boost"), model distillation strategies, and smaller purpose-built language models for distinct customer service journeys. The roadmap also includes new model customization approaches to further elevate quality and efficiency while maintaining the cost and control gains established in this phase. --- ## Devyx: Azure to AWS Migration Focused on Scalability and Agility URL: https://elevata.io/en/case-studies/devyx-migracao-azure-para-aws-com-foco-em-escalabilidade-e-agilidade ## About the Company Devyx develops custom software and technology platforms for clients that need application delivery, integration, and cloud operations support. The company works from solution design through implementation, adapting delivery to each client's systems and business requirements. ## The Challenges Devyx was facing significant challenges with its current infrastructure hosted on Microsoft Azure. As the company grew and client demands increased, the need arose for a more efficient and scalable solution. They needed a way to optimize their cloud environment, improve performance, and gain greater agility to meet constantly evolving business objectives. In addition to infrastructure challenges, the application also needed modifications. A second challenge was environment separation to control costs. In the Azure environment, all environments were concentrated in a single account, sharing the same infrastructure. Costs were also a determining factor for project adoption, since cost predictability on AWS is simpler, ensuring linear tracking of consumption within the platform. ## The Solution Our approach began with running the Elevata Compass, a detailed assessment to identify gaps across the six dimensions of the AWS Cloud Adoption Framework: business, process, people, platform, operations, and security. This assessment allowed us to determine the capabilities needed for migration and develop a TCO (Total Cost of Ownership) model for the project. Based on the assessment results, we initiated the migration, building a solid operational foundation for the transition, with the goal of addressing the identified capability gaps. In this phase, we accelerated Devyx's migration decisions by providing clear guidance on migration plans and increasing the project's chances of success. Elevata completed Devyx's migration from Azure to AWS, implementing services such as EKS, EC2, and RDS for PostgreSQL, providing an optimized cloud environment, improving performance, and offering greater agility to meet Devyx's business demands. In parallel with the required infrastructure, all application changes and adaptations were made, with ideas and insights provided by Elevata to Devyx's Development team. At this point, adopting the AWS SDK to help the application interact with cloud services was vital to ensure a robust application with all the interoperability needed for day-to-day operations. Regarding environments, during the migration to AWS, a multi-account concept was adopted, ensuring effective segregation of environments (Dev, Pre-Prod, and Prod). This process was vital to ensure a secure development process for the application, since every change made to the application has a completely safe new environment for testing. ## The Results After implementing the proposed solution, Devyx now has a more scalable and optimized cloud environment with improved performance and greater agility. This effectively supports the company's growth and future demands, allowing Devyx to better serve its clients and achieve its business objectives more efficiently. Security was enhanced so that each user, whether developer or executive, has an access level appropriate to their role in the environment, eliminating unrestricted access for all users. Costs are predictable, and after migration we achieved greater cost granularity and an approximately 18% reduction compared to the previous provider. --- ## Fintech for Doctors: Omnichannel via WhatsApp Using Amazon Bedrock URL: https://elevata.io/en/case-studies/fintech-omnichannel-via-whatsapp-amazon-bedrock ## About the Company A Brazilian fintech built specifically for doctors. The platform combines technology and specialized services to simplify the operational side of a medical career, helping professionals structure and manage their professional activity, issue invoices, organize day-to-day financial flow, and stay current with tax and accounting obligations — all from an experience designed for the reality of doctors. Beyond financial solutions, the company positions itself as a community for doctors: a space that connects professionals through shared challenges, knowledge exchange, and a support network that enables them to move forward with more agility and confidence. With strong execution pace and a product-led approach, it continues expanding its ecosystem to improve how doctors in Brazil handle the financial and administrative management of their careers. ## The Challenges Before this project, client onboarding happened through a web flow and relied heavily on manual processing. Users submitted documents and information online, and the team was responsible for validating data, confirming identity and professional registrations, and reviewing profile questionnaires. Over time, this model created friction at two critical points: user experience and internal operations. From the user's perspective, the process was slower than it should be at a moment of high intent. When someone decides to sign up, any delay increases the risk of abandonment and frustration. Internally, manual validation raised operational costs and made onboarding volume difficult to scale. Additionally, collected data did not flow consistently to CRM workflows, generating rework, manual reconciliations, and inconsistencies between systems. The goal was to bring onboarding to a channel where users already are in their daily lives — WhatsApp — while maintaining the rigor needed for document and business identity validation, and simultaneously creating a foundation that could be extended to other journeys throughout the client relationship. ## The Solution Elevata designed and implemented an AI-based omnichannel onboarding capability, connecting the client's backend to WhatsApp and establishing a foundation ready to incorporate new channels over time. The aim was not simply to add a chatbot, but to build a complete onboarding system: capable of guiding the user step by step, validating information as it is provided, and triggering business rules reliably. At the core of the solution is an architecture that processes WhatsApp messages through structured flows and executes onboarding logic via triggers. Conversation journeys are defined as flow files (in YAML), where the client describes what questions should be asked, what validations should occur, and how the flow should evolve based on user responses. Each message sent generates an event that can validate the response, call backend tools, and decide the next step in the journey. This ensures determinism where it is essential — data capture and validation — without compromising the naturalness of the user experience. To enable channel integration and ensure future extensibility, Elevata used Amazon Connect as the channel layer, enabling WhatsApp initially and creating a clear path to support web and voice interfaces in the future without redesigning the core logic. Business rule execution was implemented with AWS Lambda, while runtime services were hosted on Amazon ECS. Natural language capabilities were provided by Amazon Bedrock, treating model selection as an engineering decision driven by latency and task complexity — rather than a one-size-fits-all choice. Within Bedrock, Elevata implemented a two-model strategy to balance responsiveness and quality. Amazon Nova Micro was used for interactions that do not require complex reasoning, such as entity extraction, value normalization, and tool execution within the onboarding flow, with response times in the 500ms range. For steps requiring deeper reasoning, greater precision, and better language quality — such as summarization and information correction across a longer conversation — Claude 3.5 Sonnet was used, with typical responses around two seconds. This split maintained an agile user experience without sacrificing the accuracy needed at more critical moments in the process. Equally important as the initial delivery was ensuring the solution could evolve. The flow-oriented model makes creating new journeys easy: simply define a new flow file, register the corresponding trigger, and test behavior before release. This way, the client can expand beyond onboarding without rebuilding the solution's foundation. ## The Results The client evolved from a manual, web-form-based onboarding to a WhatsApp-first flow designed to increase completion rates and scale operationally. By validating information in real time and structuring the experience as a guided conversation — rather than a static form — the process becomes simpler for users to complete correctly and more consistent for operations. From a business perspective, the change significantly reduces the operational effort associated with manual handling and creates a cleaner path to integrate onboarding data with CRM and backend systems. The architecture was also designed to go beyond WhatsApp: while it is the first channel, the channel layer and flow framework allow expansion to web and voice as the customer experience evolves. The remaining work is production release and deeper backend integration, including CRM-related workflows, so onboarding can run with automation and operational control owned by the client. Additionally, the client and Elevata have already mapped new journeys to reuse the same framework, such as document verification extensions and accounting-related processes, digitizing more stages of the client lifecycle without needing to rebuild the solution from scratch. --- ## Grupo MBP: Toward a Digital Future with an Analytics Partnership URL: https://elevata.io/en/case-studies/grupo-mbp ## About the Company Grupo MBP, founded in 1944, is among the largest companies in Brazil. With a history of constant innovation, the company stands out in diverse segments including civil construction, metallic structures, refrigeration, and agribusiness. Its commitment to quality and sustainability has been a hallmark in delivering technology products and innovative solutions to the Brazilian market. With a diversified portfolio and a team of specialists, Grupo MBP is dedicated to serving with excellence the varied needs of its clients and projects. ## The Challenges Grupo MBP faced significant challenges implementing an integrated system spanning multiple platforms to enhance its production control, finance, billing, and enterprise resource management, as well as improving its reporting and control systems. Originally planned for one year, the project extended to two years due to implementation challenges and an unclear understanding of the delays. Identifying these gaps and issues became crucial for the project's success. ## The Solution Elevata stepped in with the Elevata Compass solution to analyze Grupo MBP's IT environment. Elevata Compass is a strategic tool that performs a detailed IT environment diagnosis, identifying gaps and challenges while mapping optimization opportunities and technological advancement. The solution offers strategic guidance, customized assessment aligned with business objectives, multi-cloud expertise, resource optimization, and cost efficiency. The Elevata Compass process involved a collaborative evaluation of Grupo MBP's goals and constraints, a detailed technical analysis of the existing environment, and the generation of a comprehensive report, providing a practical and customized roadmap for effectively achieving the company's technology objectives. ## The Results Before partnering with Elevata, Grupo MBP faced significant uncertainties about delays and obstacles in their project. With the Elevata Compass report, they now have a clear and structured path forward. The detailed analysis provided by Elevata Compass enabled Grupo MBP to deeply understand the critical points that were preventing efficient progress in their technology implementation. With this new understanding, Grupo MBP is now equipped to make informed and strategic decisions, guiding them toward successful implementation. Although implementation of the recommended solutions is still underway, Grupo MBP has already observed a positive impact from Elevata's guidance. The partnership with Elevata represents a crucial step in Grupo MBP's journey toward technological innovation and digital success. The Elevata Compass report is not merely a diagnosis of challenges but a map for the future, providing Grupo MBP with the tools and knowledge needed to successfully navigate the complex IT landscape and ensure the success of their technology initiatives. --- ## Legaltech: Google Cloud to AWS Migration with 30% Cost Reduction URL: https://elevata.io/en/case-studies/legaltech-migration-gcp-aws ## About the Company A Brazilian legaltech that helps lawyers and legal teams win more clients, capture fees more efficiently, and gain daily productivity through technology. The platform supports legal research in the era of Generative AI, combining case law with jurimetrics to reduce manual work and enable more data-driven decisions in legal practice. Positioned at the intersection of data, artificial intelligence, and law within Brazil's legal innovation ecosystem, the company offers a technology foundation that supports everything from advanced legal analyses to day-to-day operational workflows for law firms and legal departments. ## The Challenges The platform operated on Google Cloud and, as the product scaled, clear limitations emerged related to cost, scalability, and operational consistency. The first point was cost: high and difficult to optimize without a structured rightsizing effort. The second was platform elasticity, which needed to scale up and down based on real load signals rather than just static provisioning. The third challenge was in build and deploy automation maturity across an environment with a very large number of services. When each team or repository evolves its own pipeline standards over time, inconsistency accumulates: different deployment methods, different quality gates, and different operational behaviors. At this stage of scale, this translates to slower deliveries, more complex governance, and greater operational overhead. The migration itself also had a significant dimension. The environment included approximately 150 buckets totaling roughly 20 TB of data, a large Elasticsearch cluster, multiple databases (including MongoDB, PostgreSQL, and MySQL), Kafka, network components, and approximately 200 microservices and repositories. The challenge was not just moving infrastructure, but arriving at AWS with an operational model that would make growth cheaper and day-to-day execution more predictable. ## The Solution Elevata led a migration and modernization program focused on moving the platform to AWS while simultaneously improving delivery mechanisms and infrastructure cost efficiency. A fundamental step was defining a single build and deploy standard that could be applied across the approximately 200 repositories. In practice, this meant modernizing how applications were built and deployed, then replicating that standard consistently, avoiding isolated pipelines and per-service customizations. The impact goes beyond a technical decision: consistent standards directly affect delivery speed and release confidence, as teams begin operating within governed, predictable workflows rather than bespoke processes. With delivery mechanisms standardized, Elevata performed a detailed database sizing and infrastructure utilization analysis to identify cost and performance opportunities. Database sizes and compute requirements were reviewed, with adjustments to compute and storage to reduce overprovisioning. Where appropriate, Graviton instances and Spot Instances were adopted for stateless workloads — a strategy that reduces compute costs while maintaining reliability, provided workloads are designed to tolerate interruptions. To improve scalability and avoid paying for peak capacity 24/7, Elevata implemented autoscaling based on real workload behavior, going beyond basic thresholds. Scaling decisions began considering custom metrics such as processing queue depth in addition to CPU and memory. This allowed the environment to scale more intelligently, adjusting better to actual traffic patterns. Finally, governance and consistency were built directly into the deploy process through pipeline controls. This included mechanisms such as merge blocking and automated checks to validate security and code consistency before changes were deployed. For the client, this means compliance discipline that scales with the company: the same set of rules applies regardless of which team owns a service. ## The Results The migration to AWS delivered a cost structure and operational model much better aligned with the legaltech's stage and scale. The most tangible result was a 30% reduction in total environment cost, achieved through rightsizing and strategic infrastructure adjustments, including Graviton adoption where applicable and Spot Instance usage for stateless workloads. This reduction represents not just savings but also breathing room: budget previously consumed by inefficient capacity can now be directed toward growth, new workloads, or product priorities. The platform began scaling more predictably. Queue-based, CPU, and memory autoscaling allows the environment to grow during demand peaks and contract when load decreases, reducing the operational pressure of "guessing" capacity and the risk of paying for idle resources. On the delivery side, standardizing build and deploy across approximately 200 repositories brought more consistency in execution and supported faster iteration cycles. This same work strengthened the developer experience and governance by applying consistent pre-deploy controls that elevate code quality and security discipline without relying solely on manual reviews. The operating roadmap now focuses on expanding these standards as new services are created, continuing rightsizing as usage patterns evolve, and extending scalability mechanisms with more workload-specific signals. With the foundation established on AWS, the company now has a platform ready to grow without turning every demand increase into a cost surprise or operational challenge. --- ## CredAluga: Azure to AWS Migration with ~55% Cost Reduction URL: https://elevata.io/en/case-studies/migracao-de-azure-para-aws-com-reducao-de-custos ## About the Company CredAluga is a Brazilian real estate startup that connects tenants, landlords, and agents through partnerships with local real estate agencies (B2B2C model). Combining technology and credit intelligence, the company simplifies the rental journey and has been expanding its operations nationwide. ## The Challenges Before the project, operations ran on Microsoft Azure. As the user base grew, the effects of a fragmented architecture began to impact business results: unpredictable costs (including SQL Server licensing pressure), limited automation, fragile governance across Dev, Staging, and Prod, and signs that the security posture and scale needed to mature. Without structured FinOps and without visibility by product/team, portfolio management relied on subjective judgment — which complicated roadmap decisions and cash planning. Meanwhile, the company needed to segment access more rigorously and prepare the platform for peaks without turning each month into a budget surprise. ## The Solution As an AWS Advanced partner, Elevata began with a deep technical analysis — for a simple and business-driven reason: understand where CredAluga was, where it wanted to go, and which path would balance risk, timeline, and budget. This diagnostic mapped dependencies, bottlenecks, and goals (financial predictability, security, scale, and release cadence). From there, we designed an Azure-to-AWS migration plan driven by business impact, without unnecessary rework. The migration prioritized clarity and control. The foundation was organized with separate accounts by function, segmented VPCs, IAM with least-privilege profiles, and centralized logs for end-to-end auditing — translating technology into real governance. At the data layer, databases were right-sized with careful high-availability application: Multi-AZ only in production, with Single-AZ in Dev and Staging, maintaining automatic backups and read replicas when they added value. In business terms, this means paying for resilience in the right place without carrying 24/7 cost in environments that don't require that level of availability. Applications were orchestrated on Amazon EKS with Karpenter, enabling automatic elasticity and more efficient resource usage during peaks. All provisioning was standardized as code (Terraform + Terragrunt), reducing variability between environments and shortening provisioning times — which, from a business perspective, translates to more frequent releases with less risk. For security, AWS WAF, KMS, and Secrets Manager hardened the edge and data protection; remote access became more secure with OpenVPN tunneling and granular access policies. On the financial pillar, we structured consistent tagging and cost dashboards by product/team with variance alerts — the foundation of continuous FinOps that brings predictability to the P&L. And looking at data, we defined the analytics modernization path with Bronze, Silver, and Gold layers and serverless orchestration. This track was adopted by CredAluga, which organized over 500 GB of data aligned with products, elevating analysis reliability and decision-making speed. Across all fronts, adopting AWS services reduced manual work, increased reliability, and brought end-to-end observability — effects directly perceived in the roadmap, budget, and end-user experience. ## The Results The Azure-to-AWS migration delivered what leadership was looking for: predictability, security, and scale with efficiency. The clearest effect was financial: an approximately 55% reduction in monthly infrastructure costs while preserving production availability. Beyond savings, AWS enabled on-demand elasticity and end-to-end observability, transforming usage peaks into plannable events — not availability risks or budget overruns. Day to day, IaC and deploy standards brought stable cadence and replicable environments; the EKS + Karpenter combination absorbs peaks without endless on-call shifts; and the security posture — with protected edge, managed encryption, and secrets under control — elevated the confidence of internal teams and partners. With data organized in layers and stable pipelines, the company gained a foundation for information-driven products and future AI/ML initiatives (e.g., automating credit analyses and enriching the tenant experience). Post-migration, Elevata continues alongside CredAluga in continuous AWS evolution — enhancing FinOps, security, and data practices, and aligning a joint roadmap so the platform keeps pace with business growth with the same predictability the migration delivered. --- ## Rumo Tecnologia: Infrastructure Modernization to Reduce Latency URL: https://elevata.io/en/case-studies/modernizacao-de-infra-na-aws ## About the Company Rumo Tecnologias, founded in 2005, specializes in web management software and mobile applications. Focused on efficiency, cost savings, and process improvement, it serves sectors such as services, retail, and industry. Its solutions are delivered through a robust ERP system that includes modules for finance, purchasing, inventory management, and tax management. With expansion into new markets, the need arose for modern and scalable solutions to sustain growth. ## The Challenges Rumo was facing significant challenges on its growth journey. The AWS environment that once met the company's needs eventually became an obstacle. High costs, lack of scalability, and latency issues began to undermine operational efficiency and expansion potential. These factors created bottlenecks that directly affected the services Rumo offered to its clients and put its expansion plans into new markets at risk. The need to address these issues was urgent, as Rumo's continued growth depended on infrastructure capable of supporting both current and future operations with security and flexibility. ## The Solution After an in-depth analysis during the discovery session, Elevata identified a clear need to modernize Rumo's environment. The focus was on aligning the technology infrastructure with business goals, creating a solid foundation to support not only current operations but also future expansions. The modernization began with implementing Infrastructure as Code (IaC) practices, which brought robust automation to resource provisioning. This reduced operational costs and eliminated manual errors, while also bringing significant agility in adapting infrastructure to business needs. As part of the solution for latency issues, Elevata also led the migration of Rumo's environment to a new AWS region, strategically chosen to optimize proximity to the company's key markets. This measurably reduced response times and improved the user experience. The environment was reconfigured to follow AWS best practices, with a special focus on security, scalability, and resilience. This new setup not only met immediate needs but also paved the way for Rumo to grow with confidence, knowing that its infrastructure is now ready to face future challenges. Finally, application migration was carried out smoothly, ensuring stable and secure operations. And to ensure that Rumo has full autonomy in managing the new environment, the entire team was trained to continue improving and expanding the infrastructure as needed. The modernized environment now offers a strong foundation for growth, and the trust-based relationship established with Rumo was an important step in ensuring their continued success. ## The Results With Elevata's work, Rumo now has a modernized environment prepared to sustain its expansion needs. The infrastructure, aligned with AWS best practices, offers greater scalability, security, and resilience, providing Rumo with the technological foundation needed to continue growing efficiently. Additionally, migration to a new AWS region resulted in a 10% reduction in latency, measurably improving the user experience. The optimization process also led to a 10% reduction in operational costs, bringing greater efficiency and a lower operating cost base. Thanks to the adoption of Infrastructure as Code (IaC), Rumo can now manage its resources in an automated and consistent manner, bringing agility for future adaptations and allowing the company to focus on continued growth without infrastructure concerns. --- ## Venture Capital LATAM: AI Accelerates Investment Intelligence URL: https://elevata.io/en/case-studies/plataforma-ia-aws-venture-capital-automacao ## About the Company A venture capital fund whose investment workflows depend on fast access to institutional knowledge — deal history, partner discussions, due diligence notes, internal decisions, and portfolio updates. As with many VC teams, the fund's operational context is distributed across the tools used daily by partners and analysts: Slack for conversations, Notion for decision records and internal notes, Google Drive for decks and documents, and Affinity CRM for deal tracking and relationships. As the fund scales, maintaining continuity across these sources becomes essential for decision quality, execution speed, and onboarding new team members — without relying on manual information transfers. ## The Challenges The fund's internal knowledge was scattered across multiple systems, making it time-consuming to reconstruct context for answering a single question. Information existed in different formats and locations — Slack threads, Notion pages, Drive folders, and CRM records — and frequently, the team needed to switch between tools or "ask someone" to piece together the full story behind a deal, decision, or relationship. Before the project, a typical context search took between 9 and 12 minutes per question. Beyond time loss, the fragmentation created concrete operational delays: important details could be overlooked, the historical rationale for decisions was not always easy to trace back to the original source, and onboarding new team members required additional effort to transmit knowledge that already existed — but was not consistently accessible. At the same time, the solution needed to meet strict privacy and access control requirements. The fund required the assistant to operate entirely within its own AWS environment and that responses always be based solely on documents each user was authorized to view. ## The Solution In partnership with AWS, Elevata designed and implemented a GenAI-based knowledge assistant operating directly within Slack, capable of retrieving reliable, permission-aware context from the fund's internal systems. Rather than training a custom model on the fund's data, Elevata implemented a Retrieval Augmented Generation (RAG) architecture. In this model, the assistant retrieves relevant internal content at query time and uses that retrieved context to generate the response, keeping data under control and respecting existing permissions. ### **Architecture decisions and model selection** Elevata evaluated multiple foundation model options available via Amazon Bedrock and SageMaker JumpStart, focusing on natural language understanding, contextual response generation, and secure integration with private corporate data. Criteria prioritized the ability to summarize investment and business context clearly, support information retrieval from multiple sources with document-level security, and operate entirely within the fund's AWS account to meet privacy requirements — while offering a Slack-native experience to maximize adoption. The final architecture combined: - Amazon Bedrock for language generation tasks - Cohere for semantic embedding generation - Amazon OpenSearch Service for vector search and metadata filtering - RBAC in OpenSearch, applied during retrieval to ensure each user accesses only authorized content - AWS Cognito for JWT-based authentication, integrated with the permission model This approach ensured access control was applied before any context reached the model, preventing accidental leaks and keeping responses always anchored in permitted sources. ### **Building the knowledge layer with retrieval and metadata** Rather than fine-tuning an LLM, Elevata built a retrieval layer over the fund's existing content — documents, Slack threads, and CRM records — enriched through chunking, embeddings, and metadata tagging. Insights was indexed with a schema that enables filtering by source, owner or user group, tags, and date, improving retrieval quality and ensuring response traceability back to the original source. A dedicated Amazon OpenSearch cluster was configured with k-NN vector indices to enable semantic retrieval, along with custom ingestion pipelines to embed and store documents as they were ingested. RBAC rules were tied to user identity, allowing the same question to yield different results based on permissions — without changing the user experience. ### **Data integration with the fund's stack** To create a unified knowledge experience, Elevata synced the assistant with the fund's core systems: - Slack (channels and threads) - Notion (notes, policies, and decision records) - Google Drive (decks, documents, and spreadsheets) - Affinity CRM (deals, contacts, and due diligence records) With these sources indexed in a common retrieval layer, the assistant could answer questions by combining cross-tool context — for example, retrieving Slack discussion history, validating the current deal stage in Affinity, and linking the response to the specific decision record in Notion that documented the rationale. ### **Runtime flow and iteration** In practice, users interact with the assistant directly in Slack. A natural language question triggers retrieval of relevant passages in OpenSearch, filtered via RBAC. The retrieved context is then sent to a model on Amazon Bedrock, which generates a response based exclusively on those sources. Elevata conducted two iterative rounds of testing with analysts and partners to refine prompts, improve retrieval precision, and validate that responses consistently respected RBAC rules and the fund's internal tone. This iteration focused on accuracy, latency, and edge case handling, ensuring the assistant was useful in real investment workflows — not just controlled scenarios. ## The Results The fund now has a centralized, Slack-native assistant capable of delivering relevant internal context in seconds — without the team needing to switch between tools or rely on informal knowledge transfers. The impact was immediate on daily investment operations, where speed and continuity are fundamental. After deployment, the fund observed measurable gains: - ~75% reduction in time spent on search and internal alignment, accelerating decision-making - Response time under 2 seconds per query, enabling real-time use in Slack - 94% understanding accuracy and 89% contextual coherence, according to internal evaluation - 92% weekly usage by the core team, indicating high adoption among analysts and partners - First-question resolution rate of 0.85, reducing repeated searches and manual follow-ups Beyond speed gains, the assistant significantly improved onboarding and institutional knowledge continuity by making context easier to retrieve and verify. Since responses are always based on metadata-tagged retrieved documents, the team can trace each response back to its original source, strengthening documentation discipline and reducing dependence on individual memory. A Phase 2 roadmap is already mapped. With the permission-aware foundation established in Phase 1, the fund can expand the assistant to more proactive, action-oriented workflows, such as deal alerts, follow-up reminders, tag updates, and other execution loops directly in Slack — helping the team advance opportunities with less manual coordination. --- ## PoolPay: Modernization and Scale on AWS URL: https://elevata.io/en/case-studies/poolpay-modernizacao-aws ## About the Company PoolPay is a São Paulo-based fintech offering a Payments as a Service platform for digital businesses. Integrated into the checkout of major companies, its solution enables connecting multiple payment providers and methods such as PIX, credit card, boleto, and more — with intelligent orchestration, anti-fraud systems, SPLIT, and recurring billing. PoolPay focuses on payment performance, security, and conversion for businesses and consumers that need reliable checkout and settlement flows. ## The Challenges Despite already operating on AWS, PoolPay faced barriers to scaling its infrastructure with security and predictability. With the company's accelerated growth and onboarding of new clients, challenges emerged such as: - Limited Scalability: lack of a modular and standardized architecture. - Cost Visibility: difficulty in breaking down expenses and planning accurately. - Governance and Security: need to strengthen practices aligned with AWS guidelines, such as PCI-DSS. - Low Automation: absence of modern CI/CD pipelines and infrastructure as code best practices. The company needed a more robust and modern foundation to support future integrations, transactional growth, and continuous innovation without compromising security or performance. ## The Solution Elevata, an AWS Advanced Tier Services Partner specializing in startups, was chosen to lead this process. The journey began with Elevata Compass, our technical assessment offering that deeply evaluates the client's environment. Through Compass, we conducted a complete analysis of PoolPay's infrastructure, mapping points of attention, operational risks, and improvement opportunities in architecture, security, performance, governance, and cost areas. The result was a detailed diagnosis, a customized modernization plan, and a prioritized backlog with practical recommendations aligned with AWS best practices. This process not only provided technical visibility into the current environment but also prepared decision-making for modernization work with greater agility and security. With Compass insights in hand, we moved to execution with Elevata Accelerate, implementing a secure, scalable, and automated Landing Zone with Terraform. This new foundation included networking, policies, centralized monitoring, and security configurations prepared for PoolPay's future. **The new infrastructure enables:** - Environment segregation (e.g., production, staging, development). - Secure expansion with a modular and adaptable foundation. - Resource and cost optimization with modern IaC practices. - Compliance with standards such as PCI-DSS. - Use of containers (EKS) and databases (RDS) optimized to handle peaks and scale more effectively. - Use of Helm charts for better control and versioning of applications. - Build and deploy processes (CI/CD) for greater automation and minimizing production errors. ## Results PoolPay now has a solid foundation on AWS, ready to support business evolution. The new infrastructure delivered immediate gains in governance, cost visibility, and security, while preparing the company for future expansion and increased scale. The environment was designed to grow alongside PoolPay's ambitions without creating technical or operational bottlenecks. During the process, PoolPay was also able to explore resources offered by AWS and Elevata, accessing programs and incentives aimed at accelerating growth. The combination of precise technical assessment and rapid execution helped the fintech solve structural challenges and move forward with a stronger product and infrastructure foundation. --- ## Trampay: Fintech Platform Migration from Google Cloud to AWS URL: https://elevata.io/en/case-studies/trampay-migracao-de-plataforma-fintech-do-google-cloud-para-a-aws ## About the Company Trampay is a Brazilian fintech focused on the financial needs of delivery drivers and motorcycle couriers. Through the app, workers can open a free account, receive payments via Pix, pay bills, and access credit designed for their daily reality. Currently, the platform serves over 58,000 active accounts, with R$191 million already advanced in credit and over R$2 billion moved in receipts and payments made directly within the Trampay ecosystem. For companies and transport operators, Trampay acts as a financial layer of the mobility ecosystem, enabling payments, transfers, advances, benefits, and working capital flows, always focused on operational simplicity, great user experience, and social impact. ## The Challenge Trampay needed to migrate its platform from Google Cloud to AWS while maintaining service stability throughout the transition. The existing environment had a broad set of production assets — 149 resources — including a Kubernetes-based application layer and multiple services that could not be migrated in a single cutover without significantly increasing operational risk. The migration required careful planning and a solid networking foundation. Connectivity between environments needed to be established early to allow progressive service movement, validate behavior under near-production conditions, and reduce the risk of disruptions as workloads were transferred. ## The Solution Elevata implemented a phased migration strategy designed to move workloads in a controlled manner while preserving operational continuity. The first step was defining the destination foundation on AWS and establishing connectivity between the two cloud environments, allowing services to be migrated gradually rather than in a complete switchover all at once. At the container layer, workloads running on Google Kubernetes Engine (GKE) were migrated to Amazon Elastic Kubernetes Service (EKS). This included building the cluster on AWS and aligning the platform with the networking, firewall, and routing standards required to support a stable production environment. The migration also covered application services and their supporting components. The project addressed workloads that previously ran as 37 Cloud Functions, supporting a refactoring process when necessary, and introduced a clearer governance model through Infrastructure as Code (IaC) with Terraform, using a modular approach. In parallel, Elevata strengthened the security and operational posture of the AWS environment, including traffic protection with AWS WAF and database layer modernization using Amazon Relational Database Service (RDS) for SQL workloads. *"Elevata's support in our infrastructure migration was essential. We managed not only to carry out a migration with zero impact on the end user, but also to arrive at a final environment that is more secure and more optimized than the previous one."* Vitor Quaresma, CTO at Trampay. After the migration, Trampay continued working with Elevata through Orbit, Elevata's flexible AWS support model, used for ongoing infrastructure and operations activities — such as monitoring improvements, alerts, and platform integrations — ensuring environment evolution without diverting the internal team's focus from product priorities. ## The Results Trampay completed the migration of its core platform from Google Cloud to AWS using a phased, service-by-service approach that reduced migration risks and enabled controlled validations throughout execution. The Kubernetes layer was successfully migrated from GKE to EKS, sustaining a platform that today processes billions of reais in financial transactions, while the AWS environment was established on a networking foundation designed for stability and scalable operations. Beyond workload movement, the project strengthened how the platform is operated. With the adoption of Terraform-based Infrastructure as Code and a clearer security and routing foundation, Trampay gained greater consistency in deployments and environment management. Security guardrails and database layer modernization contributed to a more production-ready foundation on AWS, while Orbit ensured post-migration evolution continuity, covering the day-to-day operational work needed to keep the platform healthy as the business grows. Platform maturation continues through Orbit, with operational improvements and additional modernization initiatives prioritized according to Trampay's roadmap and growth needs. --- ## AI-Powered Travel Search and Ranking with MCP + RAG on AWS URL: https://elevata.io/en/case-studies/ai-powered-travel-search-and-ranking-with-mcp-rag-on-aws ## About the Company A leading Canadian online travel agency specializing in discounted vacation packages, flights, hotels, and cruises. The company supports Canadian travelers through its online booking experience and a team of 150+ travel agents, offering services such as last-minute deals and travel financing. The agency is a licensed retail travel agency in Ontario, governed by the Travel Industry Council of Ontario (TICO), with customer travel funds protected in Ontario by TICO and in Quebec by OPC. ## The Challenge The agency offers a broad set of travel products through its website, including vacation packages. For the vacation packages experience, the search and ranking layer was constrained by the underlying data model and retrieval approach. Package results could be filtered using structured attributes exposed by the vacations/packages API, but critical traveler intent often lives in unstructured partner content — hotel descriptions and amenity lists delivered as files and stored separately. Without an AI-powered retrieval layer that can index and search this content semantically, those details couldn't be used effectively to match intent at query time. In parallel, ranking logic could not reliably incorporate business signals such as "top picks" or recent booking momentum as first-class ordering criteria. As a result, highly desirable packages could appear on later pages while lower-performing options surfaced earlier. Technically, the system lacked a unified mechanism to combine: (1) real-time package availability and pricing, (2) AI-powered semantic matching against unstructured hotel content, and (3) deterministic ranking boosts based on the agency's performance metrics. ## The Solution Elevata addressed these constraints by introducing a search architecture that supports natural-language intent, matches that intent across both structured and unstructured data, and returns results ordered using the agency's business signals — while preserving real-time pricing behavior from the source API. To deliver this, Elevata implemented an MCP (Model Context Protocol) server on AWS that leverages Bedrock models, sitting between the user experience and the agency's internal data sources, enabling AI-powered natural-language travel search with business-aware ranking. The MCP server was built in Node.js and deployed on Kubernetes within the agency's AWS environment, reusing existing CI/CD pipeline and operational patterns. A key part of the solution is the Retrieval Augmented Generation (RAG) pattern used for hotel content. Partner descriptions and amenity data stored in S3 are ingested into a Bedrock Knowledge Base, allowing the MCP server to retrieve the most relevant content chunks at query time based on the traveler's natural-language intent. That retrieved context is then used to drive matching and filtering across packages. Once candidate packages are identified, the MCP layer applies the agency's business logic for ordering results. The same query can return packages ranked using internal performance signals — such as top picks and recent booking momentum — so high-performing inventory is surfaced earlier in the customer journey rather than being buried on later pages. Pricing remains authoritative to the agency's platform: every query triggers a call to the packages API to retrieve the current price at the moment of search, ensuring the experience reflects real-time pricing changes without maintaining a separate pricing cache inside the MCP layer. ## Results With the MCP layer in place, the agency introduced an AI-powered search experience that lets travelers describe what they want in plain language and receive results matched against hotel descriptions and amenities — without navigating complex filter combinations. Because the experience preserves real-time pricing from the packages API, customers see current prices at the moment they search. For the agency, the most immediate impact is improved discoverability and merchandising control. Results can now be ranked using internal business signals — such as top picks and recent booking momentum — so high-performing packages are easier to find earlier in the journey instead of appearing on later pages. This aligns product discovery with commercial priorities while keeping the experience grounded in customer intent. Operationally, the MCP server establishes a repeatable foundation for expanding this capability across the site. Because the implementation aggregates multiple data sources through a single interface and runs in the agency's Kubernetes environment using existing deployment pipelines, the same RAG-backed approach can be extended beyond vacation packages to other experiences — such as hotels-only or flights-only — while maintaining consistent ranking logic, real-time pricing behavior, and operational governance. --- ## Disaster Recovery on AWS for a High-Availability Tourism Booking Platform URL: https://elevata.io/en/case-studies/disaster-recovery-on-aws-for-a-high-availability-tourism-booking-platform ## About the Company A Brazilian holding company focused on the sustainable management of tourism, entertainment, and leisure attractions. The group operates iconic natural and cultural assets in Brazil, including a landmark urban park in Rio de Janeiro and two natural parks in Rio Grande do Sul — destinations tied to high-volume visitor demand and time-sensitive reservation flows. ## The Challenge The group needed a disaster recovery strategy to mitigate downtime risk in its reservation system. The environment was mixed: parts of the platform ran on Microsoft Azure and other components ran on dedicated or on-premises infrastructure. Designing DR in that context required more than replicating disks — it required a cohesive strategy across applications, APIs, and databases, with a clear failover approach that the team could execute reliably. One of the most challenging constraints was the database layer. The source database version and environment constraints created compatibility issues with standard DR agents and some managed automation paths. That meant the DR design could not rely entirely on out-of-the-box replication mechanisms; parts of the approach required custom handling to ensure data synchronization could occur safely and consistently. ## The Solution Elevata designed and implemented a DR strategy on AWS that combined AWS-managed services with targeted customization where compatibility constraints required it. For the database replication track, Elevata used AWS Database Migration Service (DMS) to replicate data into AWS. This provided a controlled, service-backed replication mechanism while supporting the operational processes needed for failover. For failover execution, Elevata implemented DNS-based switching using Cloudflare, so that in a disaster scenario traffic could be pointed to the recovery environment with a defined cutover mechanism rather than manual, ad-hoc steps. The most complex work centered on the source compute environment and OS/kernel constraints. Due to limitations in the source images and kernel compatibility, Elevata needed to create AWS-native base images using a compatible kernel version to ensure the applications could run correctly in the recovery environment. That base-image work became a foundational dependency for reliable recovery. From there, Elevata implemented a partially automated DR deployment model delivered via Infrastructure as Code. The DR environment provisioning includes automated creation of the required instances and resources, using the base images as the standard runtime. A custom script was implemented to synchronize disk data, convert it into a format the target applications expect, mount volumes, and ensure instances come up already provisioned with the data required to run. ## Results The client established a disaster recovery environment on AWS to reduce downtime risk for its reservation platform and create a repeatable recovery path aligned with the operational reality of high-demand tourism assets. The engagement resulted in a DR setup that can be activated through a defined failover procedure, with infrastructure provisioning automated through Infrastructure as Code and supported by standardized base images to ensure application compatibility in the recovery environment. On the data side, the recovery design included continuous replication using AWS Database Migration Service (DMS), with failover execution supported through DNS switching via Cloudflare. This approach gives the team a clear cutover mechanism during an incident: failover is driven by traffic redirection to the AWS recovery environment rather than manual reconfiguration under pressure. As a result, the client now has an AWS-based recovery environment that can be exercised and maintained over time, with a documented deployment approach and a controlled failover path for core services. The DR operating model now focuses on periodic failover tests and runbook evolution, keeping recovery readiness aligned with production system changes. --- ## Securing and Governing AWS for a Leading Intellectual Property Law Firm URL: https://elevata.io/en/case-studies/securing-and-governing-aws-for-a-leading-intellectual-property-law-firm ## About the Firm A Brazilian law firm specialized in Intellectual Property, with 14 partners and over 60 professionals supporting clients across advisory, prosecution, and litigation. The firm traces its roots to a pioneering IP practice with activities dating back to 1919, and has since grown into a structured, full-service IP operation. A notable part of the firm's operating model is its investment in internal systems and continuity: the firm maintains and continuously improves its database and IT infrastructure, including an internally built system containing trademarks and patents filed and registered with the Brazilian PTO — enabling access even when public systems are unavailable. ## The Challenge For a law firm, cloud decisions translate directly into business risk and service continuity. The firm needed an AWS foundation that supported three priorities: stronger governance over how the environment is managed, tighter control over what is exposed to the internet, and ongoing cost discipline as data volumes grow. As workloads expanded, the firm wanted clearer structure around identity and access, centralized audit visibility, and a network posture aligned with the expectations of a high-trust industry. In parallel, the firm needed operational patterns that reduce friction for change — so updates can happen predictably without creating avoidable downtime. Storage costs were also a concern, particularly for data that must be retained but is not accessed frequently, where a better tiering approach could reduce recurring spend. ## The Solution Elevata implemented a governance and security baseline on AWS designed to make the firm's environment easier to manage, safer to operate, and more predictable over time. The work started with governance using AWS Control Tower, establishing a structured baseline and guardrails for how the environment is organized. For access management, Elevata implemented IAM Identity Center, centralizing how user access is administered. Auditability was strengthened through AWS CloudTrail, improving visibility into account activity and supporting internal and external audit requirements. At the network layer, Elevata reviewed and tightened security group rules, removed unused resources, and reduced broad public exposure patterns. A key architectural improvement was placing EC2 workloads that previously had direct public IP exposure behind a Network Load Balancer (NLB). This creates a controlled entry point for traffic and reduces direct exposure of backend instances. It also improves operational flexibility: the firm can update or replace instances behind the load balancer without changing the service endpoint. To increase resilience, Elevata configured this load balancer approach across multiple Availability Zones, reducing dependency on a single zone. Finally, Elevata implemented S3 lifecycle policies to align storage tiers with access patterns, reducing spend on data that must be retained but does not need "hot" storage. ## Results This engagement strengthened the firm's ability to operate a cloud environment with the discipline expected in a legal practice — where continuity, control, and risk management matter as much as functionality. From a business standpoint, the most direct outcome was reduced operational risk. Centralized governance and identity management improved how access is controlled and reviewed, while audit logging strengthened visibility for compliance and internal accountability. Network hardening and the move to a load balancer model reduced unnecessary exposure, supporting a safer posture for workloads that underpin internal systems and client-facing operations. The changes also improved service continuity and change management. With a stable entry point in front of compute resources, the firm gained a deployment pattern that supports safer updates: backend instances can be rotated or replaced without changing the public endpoint, and scaling becomes possible without redesigning the front door. Multi-Availability Zone configuration increases resilience by reducing the likelihood that a localized infrastructure issue disrupts availability. On cost discipline, storage lifecycle management introduced a more sustainable model for retention-heavy environments: data can be kept securely while reducing the recurring cost of storing infrequently accessed content in high-cost tiers. Over time, this supports better budgeting and a healthier run-rate as data volumes grow. --- # Solutions ## Data & AI URL: https://elevata.io/en/solutions/data-ia Design and scale AI solutions on AWS, when and where they make sense. ## Build AI on AWS with control. We help organizations move from AI experiments to production systems with governance, performance, and predictable economics in place. From data foundations and RAG to agent systems, MCP architectures, scalable inference, and advanced model engineering, we build production AI systems on AWS with governance, performance, and predictable economics. ### Data Foundations for AI Build governed, scalable data foundations that make AI reliable and production-ready from day one. ### Cost Predictability Align infrastructure and AI spend to real usage — enabling sustainable growth and controlled scaling. ### Improve Customer Experience Activate data across channels to deliver faster, more consistent AI-powered customer interactions. ### Boost Productivity Embed AI into workflows to reduce manual effort and speed up operational decisions. ### Agentic AI Systems Build production-grade agents that support operations with clear controls, ownership, and measurement. ## How Elevata Helps ### Data Modernization & AI Foundations We modernize AWS data environments to support production AI from day one — including secure data platforms, governance models, and architectures built for RAG, agents, and large-scale inference. ### RAG & AI Application Architecture We design and implement RAG systems and AI-driven applications on AWS — securely connecting enterprise data to foundation models with performance and economic control. ### Agentic AI & MCP Implementation We design agent-ready architectures — including orchestration, tool-calling, guardrails, runtime design, and MCP integrations — enabling safe, scalable deployment of AI agents in production. ### Scalable Inference on AWS For organizations outgrowing per-token APIs, we implement capacity-based inference architectures on AWS — delivering lower latency, predictable unit economics, and stronger security boundaries; one documented customer case reduced inference costs by 35%. ### Knowledge² Partnership for RAG & Agents Through our partnership with Knowledge², we bring the K² platform to expert-agent and high-performance RAG projects on AWS, with optimized retrieval, cited answers, and deployment inside your VPC or other controlled private environments. ## Why Elevata? ### Strategic Approach We begin with a clear assessment of your AI maturity, data landscape, and operational constraints. Our engagements define what should move to production — and how — before scaling investment. ### AI-First Engineering Our team combines AWS architecture expertise with advanced AI implementation experience, including agent systems, RAG pipelines, scalable inference, and performance-driven GenAI architectures. ### Governed AI infrastructure We design AI environments that prioritize governance, cost control, security, and scalability — ensuring AI growth does not compromise operational discipline. ### Training your teams Elevata gives your teams the knowledge, processes, and tools to operate the systems we help build. We provide ongoing support and training for data and AI systems. --- ## Elevata Orbit URL: https://elevata.io/en/solutions/elevata-nova On-demand AWS engineering capacity — without long-term commitments. ## Flexible AWS engineering support — when and where you need it. Orbit gives you flexible access to experienced AWS engineers to implement targeted improvements, strengthen reliability, and optimize your environment — supporting your team’s day-to-day priorities without the overhead of a full project. ### Immediate Access to Experts Get support when you need it, minimizing downtime and maximizing productivity. ### Scale up or down Scale your resources as needed, without hiring worries. ### Cost-Effective Solution More economical than hiring full-time employees, with predictable billing. ### Fill Knowledge Gaps Find the right personnel expertise to drive your projects forward. ### No Hiring Concerns No need to worry about finding or training personnel—we provide the best talent for your team. ## How Elevata Orbit Helps ### Comprehensive Support Elevata Orbit provides structured AWS engineering capacity tailored to your needs. Our engineers support ongoing improvements, operational stability, and focused enhancements — without the overhead of large projects or permanent hires. ### Fill Knowledge Gaps When internal teams face capacity or expertise constraints, Orbit delivers experienced AWS engineers to execute with clarity and accountability — helping move priorities forward without disruption. ### Enhanced Efficiency Free your team to focus on strategic initiatives while we handle platform optimizations, reliability improvements, and operational refinements that keep your AWS environment performing at its best. ### Team integration Our engineers work in your delivery flow, using your backlog, review process, and operating standards. ## Why Elevata Orbit? ### Understanding Your Needs We align Orbit support to your operational goals, focusing on the improvements that reduce friction, strengthen reliability, and deliver measurable impact. ### Senior AWS engineering Our senior AWS engineers bring deep hands-on experience in cloud architecture, operations, and optimization — delivering execution with accountability. ### Focus on Your Success We work alongside your team to implement practical improvements that keep your AWS environment stable, efficient, and aligned to your evolving needs. ## Elevata Orbit is Not Outsourcing Elevata Orbit offers a unique approach that stands out from traditional outsourcing models. With greater flexibility, cost efficiency, and agility, our on-demand cloud engineering services empower your team to achieve outstanding results without the drawbacks of outsourcing. **Here’s how Elevata Orbit compares:** --- ## AWS Cloud Migration Services and Consulting URL: https://elevata.io/en/solutions/migracao-de-cloud AWS cloud migration services for readiness assessment, secure landing zones, MAP-aligned planning, migration waves, AWS data migration services, governance, and controlled cutover. ## Plan AWS cloud migration services around assessment, MAP, data migration, wave delivery, and cutover A useful AWS cloud migration service starts with inventory, dependencies, business risk, funding path, landing-zone readiness, data movement, and a wave plan. Elevata helps teams move from assessment through build, migration factory execution, validation, cutover, and post-migration optimization without treating migration as a simple hosting change. For teams comparing cloud migration services on AWS, the useful question is not only who can move servers. It is who can connect the business case, AWS MAP path, secure landing zone, application waves, data migration services in AWS, DR, governance, cost controls, and post-cutover operations into one accountable delivery plan. ### AI-Ready Architecture Migrate with foundations designed for production-grade AI and future scale. ### Predictable Economics Optimize cost models for sustainable growth — including AI workloads. ### Security & Governance by Design Enterprise-grade security, compliance guardrails, and workload isolation — protecting both cloud and AI systems at scale. ### Accelerate Innovation Deploy new applications and AI capabilities faster with automated, scalable AWS architectures. ### Bring AI Workloads to AWS Move generative AI and data workloads into secure, production-ready AWS environments with full performance and cost control. ## How Elevata Helps ### AWS Foundations We build secure, scalable AWS foundations using Infrastructure-as-Code and DevOps principles — designed to support modern applications and advanced AI workloads from the start. ### Wave Planning & Delivery We group applications by dependency, risk, data movement, and cutover tolerance, then run migration waves with validation criteria, rollback paths, and owner sign-off. ### MAP & Cost Modeling We build the business case around right-sizing, reserved capacity, savings plans, migration funding eligibility, and post-cutover cost controls. ### Security & Compliance We automate security and governance using AWS guardrails and compliance frameworks, protecting both cloud and AI workloads at enterprise scale. ### Cutover, DR & Continuity We plan migration windows, data replication, DNS changes, rollback, backup, and disaster recovery so business continuity is handled before go-live. ## Why Elevata? ### Assessment before movement We use Elevata Compass to map architecture, dependencies, risks, and objectives before moving workloads. ### Senior AWS implementation Elevata’s strength lies in our people. With years of hands-on experience, our senior team handles the most complex, high-stakes cloud projects. ### Workload-by-workload migration plan We define waves by dependency, risk, data movement, cutover window, and operational owner. ### Operational handoff We leave runbooks, ownership, monitoring, and operating practices that your team can keep using after the migration. --- ## AWS Infrastructure Modernization URL: https://elevata.io/en/solutions/modernizacao-de-cloud Modernize your AWS platform, applications, and delivery foundations. **Looking for AWS data modernization?** Start with our dedicated AWS Data Modernization Services page for data modernization on AWS, lakehouse architecture, Redshift, Glue, Lake Formation, analytics platforms, governed data foundations, and AI-ready data roadmaps. This page stays focused on infrastructure, application, and platform modernization. ## Is your current AWS platform limiting performance, governance, and delivery speed? Modernizing your AWS architecture improves performance, governance, resilience, and cost control. At Elevata, we help teams rework legacy platforms, application stacks, and delivery foundations into reliable environments that scale cleanly. ### Improved Performance Modernization enhances workload efficiency and infrastructure reliability — critical for high-performance and AI-driven systems. ### Cloud-Native Innovation Re-architect applications to leverage AWS services that support scalable data processing and AI workloads. ### Cost Optimization Optimize compute, storage, and AI workload economics to reduce waste and support sustainable growth. ### Agility & Scalability Enable flexible architectures that adapt quickly to business changes and evolving AI demands. ### Architecture for the next workload Build AWS environments designed to support advanced AI, automation, and long-term technological evolution. ## How Elevata Helps ### Container & Serverless Adoption We implement ECS, EKS, and serverless architectures to support scalable applications and AI workloads — reducing operational overhead while improving elasticity and control. ### App & Database Modernization We modernize legacy applications and databases into cloud-native architectures optimized for performance, security, and AI integration — without requiring full rebuilds. ### Observability & Reliability We add monitoring, tracing, logging, and reliability guardrails so modern platforms stay easier to operate, troubleshoot, and scale. ### DevOps Enablement We embed automation and CI/CD practices that accelerate development cycles while supporting complex AI and data workflows. ### Platform Guardrails & Delivery We improve shared platform services, deployment foundations, and operational guardrails so teams can ship changes faster with less risk. ### Cost Predictability We redesign resource utilization and scaling strategies to ensure infrastructure — including AI workloads — grows sustainably and predictably. ## Why Elevata? ### Assessment before modernization Every modernization begins with a review of current architecture, dependencies, reliability, cost, and delivery constraints. Elevata Compass turns those findings into the modernization sequence. ### Resilient AWS architecture We transform workloads into resilient AWS architectures that support advanced systems, scalable AI, and secure growth. ### Senior AWS engineering Our senior engineers modernize complex AWS environments with a focus on performance, governance, and long-term architectural integrity. ### Operational handoff We leave runbooks, ownership, monitoring, and operating practices that your team can keep using after modernization. --- ## Agentic Workflows URL: https://elevata.io/en/solutions/agentic-workflows Elevata helps teams put AI agents to work on AWS with clear processes, governed data, security controls, and value metrics. We start with the work that needs to improve, the risk involved, and the result the business expects. From there, we design the architecture, data access, controls, observability, and rollout strategy required for measurable, secure adoption. ## Where agents can help with control ### Software development Codex, Claude Code, and software agents need repository permissions, tool boundaries, CI/CD integration, code review, and audit trails. ### Assisted work Claude Cowork and assisted-work experiences support research, analysis, documentation, and file-based work with desktop, workspace, telemetry, and data governance. ### Operational automation Agents can support finance, support, operations, document workflows, and internal processes when paired with business rules, system integrations, and human review. ### Bedrock and AWS governance Amazon Bedrock can centralize models, inference, IAM, observability, costs, and controls when the operation needs to remain inside the AWS environment. --- ## Claude Cowork on Amazon Bedrock URL: https://elevata.io/en/solutions/claude-cowork-amazon-bedrock Claude Cowork on Amazon Bedrock helps teams with research, analysis, documentation, and file-based work while keeping inference inside an AWS-governed path. Elevata supports adoption with a governed AWS foundation, connecting Amazon Bedrock inference, MDM configuration, workspace rules, telemetry, and cost controls. The Claude Cowork Bedrock pilot should validate use cases, permitted data, local storage, egress, updates, the Code tab, observability, and metrics before scaling to more users. ## Validate Claude Cowork on Bedrock before rollout Start with small groups, low-risk tasks, and clear success criteria. Then expand with policies, logs, costs, and feedback already operating. --- # Blog ## Your AI strategy cannot depend on someone else’s permission URL: https://elevata.io/en/ai-strategy-cannot-depend-on-someone-elses-permission Published: 2026-08-28 Fable, Mythos, and Cursor lost access routes because of decisions outside their customers’ control. Here is why your company needs to own the layer that lets it switch models without rebuilding everything. ## How to Run Grok 4.6 in Codex and Claude Code on Amazon Bedrock URL: https://elevata.io/en/grok-4-6-codex-claude-code-amazon-bedrock Published: 2026-08-25 How we put Grok 4.6 into Codex and Claude Code through Amazon Bedrock—and used it across 334 substantial coding requests at a 47% lower cost than Sol and 58% lower than Opus. ## Elevata and Coralogix Announce an Observability Migration Partnership URL: https://elevata.io/en/elevata-coralogix-observability-partnership Published: 2026-08-19 Elevata is now an authorized Coralogix reseller and implementation partner, combining a full-stack platform with Elevata-led assessment, migration, validation, and cutover. ## Cloud Engineer (DevOps) URL: https://elevata.io/en/vagas-cloud-engineer-devops Published: 2026-08-12 Cloud Engineer (DevOps) responsible for AWS infrastructure, automation, EKS, data pipelines, networking, observability, and hybrid environments. ## A Codex Bug Can Make GPT-5.6 on Bedrock Cost 2.5x What It Should URL: https://elevata.io/en/codex-gpt-5-6-bedrock-costs-2-5x-more Published: 2026-07-27 For one user, cache writes were 90% of GPT-5.6 Bedrock costs. Our controlled replay found the Codex bug and cut replay cost by 59%. ## One Month In: What It Takes to Operate at the Speed of AI URL: https://elevata.io/en/one-month-in-operating-at-the-speed-of-ai Published: 2026-07-24 AI-native transformation is not a chatbot or an engineering project. It is an operating-model redesign—led from the top and won in the details. ## Governing Claude Code, Desktop, and Codex behind one Bedrock gateway URL: https://elevata.io/en/bedrock-governance-claude-code-desktop-codex Published: 2026-07-08 AWS validated the gateway pattern for Claude Code and Claude Desktop. An architecture report on how Elevata extended the same control plane across Claude Code, Claude Desktop 3P, and Codex on Bedrock — with the real spend dashboard. ## Should Claude Sonnet 5 be your default model on AWS Bedrock? URL: https://elevata.io/en/claude-sonnet-5-default-model-aws-bedrock Published: 2026-06-30 A practical guide to deciding whether Claude Sonnet 5 should become your default Amazon Bedrock model, when to keep Opus 4.8, and how to measure cost per accepted result. ## Claude Tag in Slack: how it works, what it can access, and a safe AWS rollout URL: https://elevata.io/en/claude-tag-slack-aws-foundation-autonomous-agents Published: 2026-06-24 Learn how Claude Tag works in Slack, where AWS controls apply, which architectural limits matter, and how to plan a safe pilot. ## Elevata appoints Dave Lindon as General Manager URL: https://elevata.io/en/dave-lindon-joins-elevata-as-general-manager Published: 2026-06-24 Dave Lindon takes on a newly created General Manager role to strengthen Elevata's coordination across Brazil, Canada, and customer AWS initiatives. ## AWS Lambda MicroVMs for AI agents: architecture, security, costs, and when to use them URL: https://elevata.io/en/aws-lambda-microvms-isolated-execution-ai-agents Published: 2026-06-23 Evaluate AWS Lambda MicroVMs for AI-agent sandboxes and user-code execution: fit, alternatives, security responsibilities, networking, costs, Regions, and pilot criteria. ## Elevata Signs Strategic Collaboration Agreement with AWS to Accelerate AI and Cloud Modernization for SMBs URL: https://elevata.io/en/elevata-signs-strategic-collaboration-agreement-aws Published: 2026-06-16 Under the AWS Small Business Acceleration Initiative, Elevata will help Brazilian SMBs modernize, govern, and adopt production-ready cloud and AI on AWS. ## NVFP4 Inference on Blackwell SM120 GPUs: vLLM, FlashInfer & What Worked URL: https://elevata.io/en/nvfp4-inference-blackwell-sm120-gpus-what-worked Published: 2026-06-03 Field notes from serving a large ModelOpt NVFP4 model on Blackwell SM120 GPUs with vLLM, FlashInfer, FP8 KV cache, speculative decoding, and production-shaped benchmarks — including the target/drafter boundary that made the deployment stable and why the early peak did not hold under reproduction. ## Elevata Named Launch Partner for the AWS Partner Innovation Hub in Toronto URL: https://elevata.io/en/elevata-named-launch-partner-aws-partner-innovation-hub-toronto Published: 2026-06-02 Elevata was named a launch partner for the AWS Partner Innovation Hub in Toronto, bringing Sovereign AI on AWS to Canadian leadership teams. ## Claude Opus 4.8 Is a Benchmark Literacy Test URL: https://elevata.io/en/claude-opus-4-8-is-a-benchmark-literacy-test Published: 2026-05-29 Claude Opus 4.8 improves on published benchmarks, adds effort controls, ships Dynamic Workflows, and keeps Opus 4.7 pricing — and is still not an obvious blanket upgrade. A practical guide to testing it against Opus 4.7, GPT-5.5, and Amazon Nova on AWS, with cost per successful task at the center. ## Governed AI Agent Sandbox on AWS: Architecture, MCP, and Controls URL: https://elevata.io/en/governed-ai-agent-sandbox-on-aws Published: 2026-05-19 Learn how AWS-first teams can design governed AI agent sandboxes with scoped IAM, MCP tool gateways, network controls, observability, approvals, and a safe pilot path. ## AWS MCP Server: Secure, Governed AWS Access for AI Agents URL: https://elevata.io/en/aws-mcp-server-secure-agent-access Published: 2026-05-07 AWS MCP Server general availability guide for secure AI agent access to AWS through MCP, IAM, CloudWatch, CloudTrail, sandboxed tools, and safe pilots. ## OpenAI Codex & GPT-5.5 on Amazon Bedrock: AWS Setup Guide URL: https://elevata.io/en/codex-openai-agents-on-amazon-bedrock-aws-setup-guide Published: 2026-04-23 OpenAI Codex and GPT-5.5 on Amazon Bedrock setup guide for AWS authentication, regional control, audit, cost, governance, and production rollout. ## Elevata Achieves the AWS SMB Competency URL: https://elevata.io/en/elevata-achieves-the-aws-smb-competency Published: 2026-04-21 Elevata has achieved the AWS Small and Medium Business Competency, a designation for AWS Partners with validated experience serving small and medium businesses on AWS. ## Amazon Just Deepened Its Bet on Anthropic. Here Is What It Actually Means for AWS Customers. URL: https://elevata.io/en/amazon-anthropic-investment-what-it-means-for-aws-customers Published: 2026-04-20 Amazon invested $5 billion in Anthropic this month, with the option for $20 billion more. For AWS customers, the operationally relevant parts are Anthropic's Trainium infrastructure commitment and Claude Platform launching natively on AWS. ## Claude Opus 4.7 on Amazon Bedrock: Migration Notes vs Opus 4.8 URL: https://elevata.io/en/claude-opus-4-7-on-amazon-bedrock-is-not-a-drop-in-upgrade Published: 2026-04-16 AWS added Claude Opus 4.7 to Amazon Bedrock on April 16, 2026. This guide now serves as migration context: compare 4.7 against Opus 4.8, GPT-5.5, and your production evals before promoting any default. ## NVIDIA GTC 2026: What Actually Matters for AI Teams Building on AWS URL: https://elevata.io/en/nvidia-gtc-2026-what-matters-for-ai-teams-on-aws Published: 2026-04-08 NVIDIA GTC 2026 marked a decisive shift from training to inference. The Vera Rubin architecture promises 10x efficiency gains, the NemoClaw platform brings autonomous agent orchestration, and AWS was named the primary scale partner. Here is what it means for teams running AI workloads on AWS. ## Claude Code on Amazon Bedrock: AWS Setup & Rollout Guide URL: https://elevata.io/en/claude-code-on-aws-complete-guide-bedrock-setup-self-hosted-models Published: 2026-03-31 Updated Claude Code on Amazon Bedrock rollout guide covering IAM role separation, Bedrock invocation logging, model pins, SSO, workstation rollout, Terraform state, and production audit boundaries. ## Demand Generation Specialist (São Paulo) URL: https://elevata.io/en/demand-generation-specialist-sao-paulo Published: 2026-02-18 Demand Generation Specialist in São Paulo responsible for turning AWS campaigns, programs, and partnerships into qualified opportunities through disciplined execution and measurement. ## Account Executive (Toronto) URL: https://elevata.io/en/account-executive-toronto Published: 2026-02-18 Account Executive in Toronto responsible for consultative AWS and AI services sales, customer and AWS relationships, pipeline development, and account expansion in Canada. ## No-Code Generative AI: Building Automation Agents with Quick Flows and Quick Automate URL: https://elevata.io/en/no-code-generative-ai-building-automation-agents-with-quick-flows-and-quick-automate Published: 2026-02-02 By Paulo Frugis, CTO at Elevata The Productivity Paradox and the Next Evolution of Enterprise AI For the last two years, the corporate world has been locked in a “Productivity Paradox.” We have access to the most powerful Large Language Models (LLMs) in history, yet aggregate productivity has not skyrocketed as predicted. The reason? A […] ## The Cloud Paradox: Why Your Multi-Million Dollar Cloud Strategy Still Looks Like an Old School Data Center URL: https://elevata.io/en/the-cloud-paradox-why-your-multi-million-dollar-cloud-strategy-still-looks-like-an-old-school-data-center Published: 2026-02-02 The narrative of the last decade has been dominated by a singular, overwhelming directive: Move to the Cloud. For years, C-Suite executives, CTOs, and IT Directors have been sold a vision of the future that promised three things: infinite scalability, unprecedented agility, and—most enticingly—significant cost reductions. The pitch was simple. By ditching the heavy capital […] ## The Architecture of Autonomy: Why Your App Platform Can’t Handle Frontier Agents URL: https://elevata.io/en/the-architecture-of-autonomy-why-your-app-platform-cant-handle-frontier-agents Published: 2026-02-02 By Paulo Frugis – Elevata’s CTO We are witnessing a quiet but violent shift in the software landscape. For the past two years, the industry has been in the “Honeymoon Phase” of Generative AI, obsessed with Chatbots, Copilots, and RAG (Retrieval-Augmented Generation) systems that summarize PDFs. That phase is ending. The market is no longer […] ## Elevata Achieves the AWS Generative AI Competency URL: https://elevata.io/en/elevata-achieves-the-aws-generative-ai-competency Published: 2025-12-16 Elevata earned the AWS Generative AI Competency, validating our ability to design, implement, and operate production-grade GenAI solutions on AWS. ## Elevata Announces a Partnership with Escola da Nuvem to Help Build the Future of Technology in Brazil URL: https://elevata.io/en/elevata-announces-a-partnership-with-escola-da-nuvem-to-help-build-the-future-of-technology-in-brazil Published: 2025-10-04 Elevata and Escola da Nuvem are joining forces around a shared goal: prepare, connect, and create opportunities for cloud talent so the future of technology in Brazil is built on stronger access, inclusion, and real employability. ## Amazon Q Business + Zoom: Bring Company Knowledge Into Every Meeting URL: https://elevata.io/en/amazon-q-business-plus-zoom-bring-company-knowledge-into-every-meeting Published: 2025-09-30 The Amazon Q Business and Zoom AI Companion integration puts trusted company knowledge inside live meetings. It reduces context switching, respects existing permissions, and helps teams make faster, better decisions. ## Beyond the Hype: How to Turn Your Data into a Competitive Advantage with Generative AI URL: https://elevata.io/en/beyond-the-hype-how-to-turn-your-data-into-a-competitive-advantage-with-generative-ai Published: 2025-09-15 Generative AI becomes a real competitive advantage when it is grounded in proprietary data, not generic public models. This article explains where foundation models fall short, how fine-tuning and RAG create differentiation, and why a modern data strategy matters as much as the model itself. ## Overcoming Cloud Environment Challenges: A Guide for Lean Teams URL: https://elevata.io/en/overcoming-cloud-environment-challenges-a-guide-for-lean-teams Published: 2025-09-15 Managing or migrating to the cloud is a critical step for many companies, but lean teams often face unpredictable costs, weak governance, and difficulty scaling. With the right strategy, those barriers can become a foundation for growth instead of a brake on it. ## Generative AI: The Strategic Path to Efficiency, Scale, and Innovation URL: https://elevata.io/en/generative-ai-the-strategic-path-to-efficiency-scale-and-innovation Published: 2025-09-15 Generative AI is moving from proof of concept to operating model. This article outlines the shift to business value, the data foundation required, the main adaptation strategies, and a practical two-phase rollout for internal AI assistants. ## IT Environment Assessment: Why Your Digital Strategy Needs a Clear Starting Point URL: https://elevata.io/en/it-environment-assessment-why-your-digital-strategy-needs-a-clear-starting-point Published: 2025-09-15 Technology is at the center of modern business, but fast growth often leaves companies with complex, poorly documented IT environments. Before migrating to the cloud, adopting AI, or scaling digital operations, you need a clear assessment of where you stand. ## AWS Startup Programs: Activate, Credits, and Growth Paths URL: https://elevata.io/en/aws-for-startups-programs-that-accelerate-growth Published: 2025-09-15 A guide to AWS Activate, proofs of concept, Incremental Workloads, and MAP for startups at different stages of growth. ## What Are AI Agents? The Technology Reshaping Business Operations URL: https://elevata.io/en/what-are-ai-agents-the-technology-reshaping-business-operations Published: 2025-09-15 AI agents are autonomous systems that interpret context, gather data, and act toward a goal. This overview covers their core principles, architecture, common types, business value, adoption risks, and how to apply them responsibly. ## AWS Summit São Paulo 2025: Archived Event Guide URL: https://elevata.io/en/aws-summit-sao-paulo-2025-your-essential-guide-to-the-event Published: 2025-07-30 An archive of AWS Summit São Paulo 2025 covering event facts, the keynote, major tracks, and themes that remain relevant. ## Unlocking GenAI on AWS: Technology, Use Cases, and Funding Paths URL: https://elevata.io/en/unlocking-genai-on-aws-technology-use-cases-and-funding-paths Published: 2025-06-24 AWS gives teams a practical path to build GenAI applications with the right foundation for security, scalability, and responsible adoption, while incentive programs can reduce the cost of experimentation. ## Accelerating Innovation with Amazon Q Developer Pro on AWS URL: https://elevata.io/en/accelerating-innovation-with-amazon-q-developer-pro-on-aws Published: 2025-06-23 Amazon Q Developer Pro can help AWS engineering teams ship faster with more context, better code quality, and stronger governance across modern delivery workflows. ## Elevata Achieves the AWS Transfer Family Service Delivery Designation URL: https://elevata.io/en/elevata-achieves-the-aws-transfer-family-service-delivery-designation Published: 2025-04-15 Elevata earned the AWS Transfer Family Service Delivery designation, reinforcing our ability to modernize secure file-transfer workflows on AWS. ## Elevata Achieves the AWS Control Tower Service Delivery Designation URL: https://elevata.io/en/elevata-achieves-the-aws-control-tower-service-delivery-designation Published: 2025-04-02 Elevata earned the AWS Control Tower Service Delivery designation, validating our capability to implement secure, scalable multi-account governance on AWS. ## Elevata Achieves AWS Advanced Tier Partner Status URL: https://elevata.io/en/elevata-achieves-aws-advanced-tier-partner-status Published: 2025-03-25 Elevata is now an AWS Advanced Tier Partner, reinforcing our ability to help companies modernize on AWS with senior technical execution across migration, data, and generative AI. ## The Growth of Artificial Intelligence Adoption Today URL: https://elevata.io/en/the-growth-of-artificial-intelligence-adoption-today Published: 2025-03-24 Artificial intelligence has become one of the most transformative technologies in business, driven by better infrastructure, broader data availability, and a growing need for efficiency, automation, and innovation. ## Secure Cloud Migration Checklist: Core Risks and Controls URL: https://elevata.io/en/secure-cloud-migration-a-comprehensive-guide Published: 2025-03-09 Checklist for secure cloud migration covering core risks, access controls, monitoring, compliance, and ongoing hardening. For an AWS-specific guide, see Elevata's secure cloud migration page. ## To Migrate or Not? How to Evaluate a Cloud Move URL: https://elevata.io/en/to-migrate-or-not-how-to-evaluate-a-cloud-move Published: 2025-02-09 Choosing whether to move to the cloud is a consequential decision. The cloud can improve flexibility, cost efficiency, and collaboration, but it also introduces risks that need to be weighed carefully. ## How the Zero Trust Model Is Transforming Enterprise Digital Security URL: https://elevata.io/en/how-the-zero-trust-model-is-transforming-enterprise-digital-security Published: 2025-01-08 As corporate environments become more distributed and remote work becomes standard, traditional perimeter-based security models are no longer enough. Zero Trust is emerging as a more resilient way to protect users, devices, and data.