Problem
Insurance teams – SIU, retention, catastrophe risk, and distribution – need consistent answers from the same portfolio data, but conflicting metric definitions and ad-hoc SQL slow decisions.
Built for
Analysts and managers in fraud investigation, policy retention, exposure management, and producer performance who want trusted answers through conversation.
Architecture & data flow
Generated synthetic CSVs → Delta tables in insurance.gold → curated semantic views → Databricks Genie → FastAPI backend → React frontend → deployed on Databricks Apps.
What users can ask
Users can investigate providers, policy renewal behavior, geographic exposure concentration, and agent performance using natural language. They can also ask follow-ups within the same conversation to drill into results.
How Genie powers the experience
Genie is the analytical brain. A thin FastAPI proxy forwards questions, polls for results, and returns answers, generated SQL, and data. The React UI renders results, errors, and SQL transparently. No custom text-to-SQL layer competes with Genie.
What I learned
Accuracy comes from the semantic layer – documented views, column comments, explicit metric definitions, and certified example questions – not from prompt tricks. Clean governance makes Genie consistently right.
Demo: https://youtu.be/8jx_KxIA8wk
#DatabricksGenie #DatabricksApps #InsuranceAnalytics #DataGovernance
