Why it mattered
Most questions a business asks about its own records come in two halves. One half is structured: which project, which period, which category. The other half sits in free text, in the description someone typed when the record was created, because that is where the actual reason is written down.
A database query answers the first half. Semantic search answers the second. Neither answers the question, and this is where many enterprise AI pilots stall: the demo works on a clean example, and real questions fall between the two.
The records were also sensitive. Copying them into a separate AI store would have meant rebuilding access control around the copy and explaining to governance why the data had moved.
How it works
- Built next to the data. The agent runs on the data platform the records already live in. Nothing is copied into a separate vector store, so existing governance and access control keep applying unchanged.
- Hybrid retrieval. Deterministic filters on the structured fields narrow the records first; semantic search over the free-text description ranks what is left. The two result sets are merged before the model sees them.
- Grounded answers. The model answers from the retrieved records and refers back to them, so a reader can check where each statement comes from.
- Architecture and delivery. I owned the architecture and led delivery; a dedicated development team built it.
The pattern carries over to any records with fields plus free text: service tickets, maintenance logs, claims, audit findings, customer feedback.