SpeedAssassin

will go first if you have trades above 20

One problem I keep seeing with internal AI search is that company knowledge is split between two worlds. Policies, contracts, specs, and notes usually live in docs, while the actual business records live in SQL tables or SaaS tools. Basic RAG can find a relevant paragraph in a PDF, but it often has no idea how that paragraph connects to the actual customer, invoice, ticket, or database row.

What seems to matter more than just vector search is having some kind of semantic layer between the documents and the structured data. The AI needs to understand relationships, not just similar words. I’ve been testing Evose for this kind of setup because it can help sync different sources into one index instead of forcing every connector and mapping layer to be built manually. It still requires careful schema design, but it feels much cleaner than treating every data source as a separate search problem.

Curious how others are handling this. Are you building separate indexes for each department or trying to move toward one shared internal knowledge layer? Also, how are you dealing with the gap between relational data and vector retrieval?