Direction / Knowledge bases and RAG
Corporate vector knowledge base (RAG)
We consolidate documents and corporate knowledge into a managed vector base for search and AI workflows.
Business outcomes
Answers link to specific sources
Access rights are enforced for every request
Documents update without rebuilding the whole system
Unanswered questions and weak spots are visible
How we launch
- 01
We inventory sources, owners, and permissions
- 02
We define structure, cleanup, and update rules
- 03
We configure retrieval, reranking, and citations
- 04
We test on team questions and launch monitoring
Common questions
Does RAG train a model on our data?
Usually not. The system retrieves relevant passages from approved sources and provides them to the model for answering, which makes updates and control easier.
Can the answer show its source?
Yes. A link to the document, section, and retrieved passage is an essential part of a reliable corporate RAG system.
What about conflicting documents?
We define source priority, ownership, and validity dates. If a conflict cannot be resolved automatically, the system exposes it instead of hiding it.