AWS Generative AI Competency
Implement generative AI with Amazon Bedrock: RAG, agents, evaluation, supported customization paths, and guardrails. AWS Partner with Generative AI Competency and proven results.
Production decisions
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?
The challenge
Most companies can build a generative AI prototype quickly, but struggle taking it to production: enterprise data integration, latency, inference cost, data security, and governance. Amazon Bedrock solves the infrastructure, but implementing it correctly requires experience with RAG, agents, Knowledge Bases, and guardrails.
Who it's for
Companies already using or planning to use AWS that want to implement generative AI for automation, customer service, document analysis, or content generation. Ideal for teams that need help going from prototype to production with Bedrock.
Production decisions
Bedrock reduces infrastructure work, but it does not replace decisions about use case, data, evaluation, cost, and operations.
Implementation patterns
Fits summarization, classification, rewriting, and extraction with little proprietary context. The real work is validation, logging, cost, and limits.
Use when answers depend on policies, manuals, contracts, tickets, or a knowledge base. Critical decisions: data quality, chunking, permissions, evaluation, and freshness.
Use when AI needs to query APIs, create records, open tickets, or orchestrate steps. Requires permissions, approval, tracing, fallback, and strong testing.
Best for legal, financial, healthcare, or customer-facing workflows where the model helps but a person approves the final decision.
Bedrock services
RAG with Knowledge Bases that accounts for data quality, chunking, permissions, evaluation, freshness, and citations before exposing real users.
Agents for querying systems and executing steps with tool permissions, approval points, tracing, fallback, and strong testing.
Guardrails help filter content and sensitive data, but we pair them with application authorization, audit, evaluation, and human review where needed.
Model selection, routing, caching, batching where applicable, prompt/context control, and cost measurement per answer, document, ticket, or workflow. Where AWS-supported customization paths fit, we assess cost, risk, and expected gain before recommending them.
35%
inference cost reduction in a real case
250+
launches on AWS
GenAI
AWS Generative AI Competency
Related guides
End-to-end generative AI strategy on AWS.
RAG, agents, and Region decisions for Canadian teams.
Workshops and Bedrock architecture for Toronto/GTA teams.
Bedrock architecture with attention to LGPD, logs, and the São Paulo Region.
Bedrock for São Paulo companies with AWS workloads.
Design prompts, RAG, and models with predictable cost.
Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads.
Data foundation needed to feed AI models.
About Elevata
Elevata combines AWS Generative AI Competency, production RAG/MCP experience, and FinOps discipline to move Bedrock projects beyond POC without losing cost, security, or operations control.
More about usFrequently asked questions
Amazon Bedrock is a managed AWS service that provides access to leading language models (Claude, Llama, Titan) via API. It includes Knowledge Bases for RAG, agents, guardrails, and supported customization options without requiring your team to manage GPU infrastructure.
You do not need to manage model infrastructure, but you do need owners for process, data, security, evaluation, and operations. Bedrock simplifies model access; production still requires architecture decisions.
Cost depends on model, tokens, retrieved context, embeddings, number of calls, retries, fallback, logs, and test traffic. We estimate cost per answer, document, ticket, or workflow before production.
Yes. AWS documentation for Amazon Bedrock states that prompts and completions are not used to train AWS models, and that the service provides data protection, encryption, and private connectivity controls with AWS PrivateLink. Guardrails add control over topics, content, and sensitive information.
References
Note: AWS service availability, model availability, pricing, program terms, and regional support can change. Validate current AWS documentation before making production architecture decisions.
Next step
Schedule a conversation to discuss your generative AI use cases and how Amazon Bedrock can solve them.