Skip to main content
You went from an empty environment to governed P&C insurance analytics — and, more importantly, to a model that grows itself:
  • North Star set: you named what the ontology is for, picked an archetype, and chose the questions that matter — before touching the schema
  • Connected: ontology repo (live, git-backed) + warehouse (read-only) + your documents
  • Validated: the model diffed against your actual schema, the policy/coverage/claim/transaction grain settled, fixed via PR
  • Classification live: NAIC line and peril-group rollups joining in-sandbox, zero warehouse writes
  • Governed answers: loss ratio, combined ratio, frequency, severity, reserves, retention — SQL and basis shown, decisions recorded
  • Compliant by default: identifier inventory, <5 suppression, fair-pricing/anti-redlining, sensitive-claim and reserve-MNPI gating
  • Self-maintaining: not-yet-governed questions answered on the frontier and proposed back as PRs — discovery paid once, then reused
  • Yours: definitions adapted in your repo — including the written-vs-earned trap — pinned with golden-query tests

Where to go from here

  • Operate it: the Ontology Operations workshop covers the production loop — git workflows, access control, accuracy testing, scaling across teams.
  • Understand the build: Build Your Ontology, End to End teaches the from-scratch method the starter packages up.
  • Roll it out: point your actuaries, underwriters, and claims teams at Self-Service Analytics — the ontology you just stood up is what makes their answers trustworthy.
  • Licensing: before production, review LICENSING.md (ISO peril/cause-of-loss & rating territories, PCS catastrophe codes — structural only, sourced from your licensed feed).
How was this workshop? One click helps us improve — optional comment after. Thanks — feedback recorded!**’})();return false;”>😕 Not usefulThanks — feedback recorded!**’})();return false;”>🙂 OKThanks — feedback recorded!**’})();return false;”>🤩 Great