AI in Brazil: Portuguese, evaluation and the capability to choose

A Brazilian agenda that spans technology

The program announced for the third Brazilian AI Observatory (OBIA) seminar, scheduled for September 3, 2026, highlighted sustainability, risk, governance and AI education. An August 28 statement by NIC.br and CGI.br framed these within the discussion of national capability. Source: CGI.br [1].

That agenda prompts a question for Polybee: what should remain in the country, organizations and teams when an AI project is delivered? We believe the answer includes knowledge, evaluation methods and the ability to keep building.

Adopting a tool can begin that process. For lasting learning, implementation decisions must be understandable and reviewable by the people who will operate the system.

Portuguese as part of the design

Treating Portuguese as a first-class language means including it when tasks are defined. A grammatically fluent response can still misread a technical term, miss a condition in a document or overlook ambiguity in a question.

We propose evaluating Portuguese-language use cases with examples representative of the context: different ways to state a need, domain vocabulary, incomplete documents and requests that call for clarification. Examples should reflect the people who will use the system without reducing Brazilian diversity to one way of speaking.

The expected result must also be clear: what information should appear, what evidence supports it, and when the system should acknowledge that it lacks a basis to answer. Language then becomes part of measurable quality.

Retrievable knowledge requires responsibility

Brazil’s government Guide to RAG in the Public Sector describes an architecture that consults document collections before generating an answer. Retrieval-augmented generation connects a model with organizational knowledge. Source: Governo Digital [2].

For Polybee, the engineering question starts with the quality of that connection. Was the right document retrieved? Is it current? Does the quoted passage support the conclusion? Can the system reveal when information is missing?

1. KnowledgeOrigin, version and context of documents.
2. RetrievalPassages relevant to the question.
3. EvaluationVerifiable answers and explicit limits.
4. LearningLogged failures for system improvement.

Imagine a hypothetical assistant consulting manuals in a Brazilian organization. The team could start with a controlled set of documents and questions, evaluate retrieval separately from the generated answer, and log cases where a source is insufficient. This is an illustration, not a Polybee implementation claim.

Sovereignty as the ability to decide

In our view, sovereignty requires real options to choose partners, adapt tools and develop alternatives when needed. The location of a server alone does not tell us who controls data, understands the system or can maintain it.

A local-capability agenda can start with proportional decisions: keep documentation understandable, prepare Portuguese-language evaluations, organize knowledge bases and train people to interpret failures. These assets outlast an interface or model.

Brazil can contribute to global AI from its own problems, languages and contexts. For Polybee, that means connected research and engineering across models and Portuguese, data and knowledge, evaluation and execution. That is how using intelligence can become a capability to create it.