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.
AWS Generative AI Competency
Amazon Bedrock that makes it past the POC
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.
Production decisions
Before choosing a Bedrock architecture
Model access
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?
Knowledge
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?
Controls
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?
Operations
Cost and quality by workflow
How will the team measure cost per answer, document, or ticket, latency, retries, fallback, logs, traces, and evaluation quality?
After the prototype
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.
First engagement
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.
Delivery path
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.
Architecture matrix
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.
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.
35%
documented inference cost reduction in an agentic workload
5
decision patterns: prompt, Knowledge Bases, RAG, agents, and human review
GenAI
AWS Generative AI Competency applied to production
Proof and related guides
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.
View case
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.
View architecture
Travel search with RAG and tools on AWS
Search and ranking architecture with sources, tools, and evaluation to reduce risk before scaling.
View case
AWS Generative AI Consulting
End-to-end generative AI strategy on AWS.
Explore resource
Amazon Bedrock Consulting in Canada
RAG, agents, and Region decisions for Canadian teams.
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Amazon Bedrock Consulting in Toronto
Workshops and Bedrock architecture for Toronto/GTA teams.
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Amazon Bedrock Consulting in Brazil
Bedrock architecture with attention to LGPD, logs, and the São Paulo Region.
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Amazon Bedrock Consulting in São Paulo
Bedrock for São Paulo companies with AWS workloads.
Explore resource
Amazon Bedrock Cost Optimization
Design prompts, RAG, and models with predictable cost.
Explore resource
Claude on Bedrock for Canada
Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads.
Explore resource
Cloud Data Modernization
Data foundation needed to feed AI models.
Explore resource
Frequently asked questions
What do people ask about Amazon Bedrock Consulting?
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.
Architecture review
Bring your use case, POC, or cost problem
We help you decide what to keep, what needs to change, and whether Amazon Bedrock is actually the right production path for that workflow.