AWS Generative AI Competency
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.
Before architecture
Use case fit
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?
Data readiness
Current and permissioned sources
Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate?
Risk boundary
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?
Path to production
Launch criteria
Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook.
Proof before promise
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.
Fit and non-fit
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.
Technical decision
| When it fits | What to validate first | |
|---|---|---|
| Amazon Bedrock | 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. |
| SageMaker | 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. |
| Data platform first | 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. |
| Not GenAI yet | When search, rules, analytics, automation, or a simpler interface would solve the job more reliably. | User workflow, failure cost, business metric, and maintenance burden. |
From POC to production
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.
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.
Implementations with RAG, agents, integrations, security review, monitoring, and handoff commonly take 6-12 weeks or more depending on data, compliance, and integration scope.
We document runbooks, owners, metrics, alerts, prompt/model review, fallback, costs, and improvement cadence for the internal team or managed operations.
What you receive
The assessment should turn an idea or POC into usable decisions: build, narrow the scope, fix the data first, or choose a simpler solution.
AWS
AWS Generative AI Competency validated by AWS
35%
reduction in documented inference optimization work
400+
AWS launches across workloads and environments
Data and AI ecosystem
Related guides
Technical implementation details with Bedrock.
Strategy, RAG, agents, and governance for Canadian teams.
Workshops and GenAI implementation for Toronto/GTA teams.
GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region.
Generative AI for São Paulo companies with AWS workloads.
Control unit cost for Bedrock, SageMaker, and RAG.
Connect models to data, tools, and auditable workflows.
The data foundation that feeds your AI models.
Control inference costs and AI infrastructure spending.
Frequently asked questions
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Talk to Elevata
In one focused call, we help clarify whether your workflow is a good GenAI candidate, which AWS architecture is likely to fit, what data or governance gaps need attention, and what a realistic first phase should include.
