Elevata

RAG + MCP + AWS

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

Architecture decisions before you build

Data boundary

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?

Retrieval design

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?

Tool boundary

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.

Brazil Region, LGPD, and Portuguese quality

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

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

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.

Reference architecture

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.

AWS

Advanced Tier Services Partner

sa-east-1

São Paulo Region when applicable

PT/EN

bilingual Canada-Brazil delivery

Frequently asked questions

What do people ask about RAG and MCP on AWS for Brazilian Companies?

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.

Talk to Elevata

Assess a RAG + MCP architecture on AWS

Share data sources, tools, users, and Region requirements. We will respond with architecture, security, and operations points.

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