RAG + MCP + AWS
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
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 retrieves documents, policies, data, or search results to ground model answers. In production, the challenge is permissioning, freshness, chunking, evaluation, traceability, and cost.
MCP
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.
Practical architecture
The design needs to respect permissions, Portuguese-language quality, LGPD, traceability, and limits for actions in real systems.
Reference architecture
Scope
We map sources, freshness, access filters, and traceability by user or role, with attention to sensitive data and privacy requirements.
We define chunking, embeddings, retrieval, quality tests, and workflow-level metrics before taking the use case to production.
We connect tools with limits, authentication, logs, and approval to reduce the risk of wrong actions in real workflows.
We review Region, logs, cost, observability, and controls that support Portuguese-language operations and Brazilian requirements.
AWS
Advanced Tier partner
sa-east-1
São Paulo Region when applicable
PT/EN
bilingual Canada-Brazil delivery
Related architecture
See the same architecture pattern with Canada-specific Region and operations questions.
RAG, agents, Guardrails, and Region decisions for Brazilian workloads.
Measure cost per answer, ticket, document, or automation before scaling usage.
About Elevata
Elevata implements RAG, MCP, and agents on AWS with attention to permissions, LGPD, traceability, cost, and Portuguese-language operations. The focus is connecting AI to data and tools without losing control.
More about usFrequently asked questions
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.
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.
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.
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
Share data sources, tools, users, and Region requirements. We will respond with architecture, security, and operations points.