Almost every company has already tried generative AI: an assistant that answers questions, automatic document summaries, a demo that impresses the board. The hard part comes next: taking it to production, with reliable answers, predictable cost and no exposure of information that should stay where it is. In practice, what separates a flashy pilot from a useful solution is rarely the model you pick. It is the data and the governance around it.
Quality and cataloging before the model
A language model answers based on what it is given. If the same internal policy exists in five versions and nobody knows which spreadsheet is the official one, AI will repeat that confusion with great confidence. Cataloging sources, assigning an owner to each one and flagging what is current is the least visible work, and the most decisive.
RAG over internal knowledge
The most common architecture is retrieval-augmented generation, or RAG: the system searches the company’s sources for the relevant passages and gives the model only that context. This reduces made-up answers, makes it possible to cite the source and removes the need to train your own model. But quality depends on how documents are split, indexed and kept up to date. A stale index means stale answers.
AI must not know more than the user
If an employee has no access to a document, the assistant cannot use it to answer them either. Permissions must be enforced at retrieval time and inherited from the source systems. Otherwise, AI becomes a shortcut around access controls.
LGPD and GDPR by design
Personal data requires a legal basis, a defined purpose and minimization. For companies operating in Brazil and Europe, that means knowing where data is processed, what is sent to the model provider, how long it is logged and how to handle data subject requests. Masking, audit trails and clear vendor contracts are part of the solution from day one.
Generative AI is only as reliable as the data it reads, and only as safe as the permissions it respects.
From pilot to production
- Use cases with a return: start with processes that have volume, a known cost and a business owner, such as internal support, document analysis and assistance for operations teams.
- Metrics from the start: correct answers, time saved, cost per query and real adoption.
- Continuous evaluation: a reference set of questions, reviewed by experts, to catch regressions with every change of model or source.
- Real operations: monitoring, cost control, incident management and an owner for the solution after go-live.
Starting with data looks slower. In practice, it is the shortest path to generative AI that the company uses every day, not just in presentations.
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