Semantic analytics in production.

Insurance runs on questions: how is this book performing, what's eroding, does this quarter's regulatory report match last quarter's definitions? Answering them meant analyst queues, hand-built extracts, and — the quiet killer — subtly different metric definitions across domains, which is how a company ends up arguing with its own numbers.

The naive fix, "point an LLM at the warehouse," fails in a regulated industry for a reason that deserves respect: an answer you can't govern is a liability with good grammar. The real problem was never can a model write SQL — it's can the business trust what comes back, every time, within the access each person already has.

The constraint set came first: answers had to draw on governed, curated data — not raw tables; metric definitions had to be consistent across domains and with regulatory reporting; and the system had to serve non-technical users, not just analysts.

That pointed at a semantic layer rather than a chatbot — and at a marketplace rather than a monolith. The intelligence lives in authored domain skills that encode how each domain's data actually works and bridge the data catalogs into agent-ready data products. Each plugin packages the skills a domain needs; the marketplace makes them discoverable, governable, and supportable at enterprise scale. Writing skills is closer to authoring documentation for a very literal colleague than to prompt engineering — and it's where the leverage is: once claims and policy could be read together, cross-domain metrics like policy erosion went from impractical to routine.