Make company knowledge usable
Retrieval-Augmented Generation connects an AI application to information outside the model's training. The engineering challenge extends beyond putting documents into a vector database: the right evidence must reach the right user, and an answer must remain connected to its sources.
What I deliver
- A knowledge ingestion and review workflow.
- Retrieval design using vector search, structured knowledge, or an appropriate combination.
- Document provenance and citations.
- Access boundaries enforced before evidence reaches the model.
- Evaluation datasets for retrieval quality, unsupported answers, and missing evidence.
Design for the difficult questions
We examine incomplete sources, conflicting information, stale documents, and questions that cannot be answered. A useful system can say what it found, what it could not establish, and when a person should review the result.
Narravo's Living Brand Graph is a concrete example of structured business context and reviewed knowledge. It is described as its own implementation, rather than evidence that every RAG system needs a graph.
Common questions
Do we need a vector database?
That depends on the content and query patterns. Structured queries and conventional search can be sufficient for some problems.
Can private documents stay restricted?
Access control is part of retrieval design. It must be evaluated independently from the model's response.
How do you test answer quality?
Separate retrieval relevance, grounding, citations, and end-to-end usefulness. My production RAG guide explains the approach.