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.
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.
Fit check
Is Amazon Bedrock the right path for this workload?
The best Bedrock project starts with an honest decision: when the managed service accelerates production, 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.
Next step: 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.
Next step: 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.
Delivery path
What a production Bedrock delivery includes
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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.
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2. Architecture and go/no-go criteria
We define prompting, Knowledge Bases, custom RAG, agents, human review, model choice, fallback, telemetry, budget, and evaluation set.
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3. Hardened pilot
We build the first workflow with logs, tracing, guardrails, application authorization, failure testing, tool limits, and cost-per-task measurement.
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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.
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 accelerates managed models, guardrails, and AWS integration. SageMaker fits deeper MLOps and control. Self-hosted inference can win when scale, economics, or deployment constraints justify the operational burden.
Cost, evaluation, and operations
Before launch, define the evaluation set, cost per answer/document/ticket, budget, telemetry, log retention, rollback, model owner, and review cadence.
Artifacts
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.
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
Elevata combines AWS Generative AI Competency, data engineering, cloud architecture, and FinOps discipline to decide when Bedrock is the right path, when it is not, and how to move the workflow to production without losing cost, security, or operational control.
What do people ask about Amazon Bedrock Consulting?
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 phases with go/no-go criteria, 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.
Note: AWS service availability, model availability, pricing, program terms, and regional support can change. Validate current AWS documentation before making production architecture decisions.
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.
