How We Found $5.8M in Hidden Revenue Leakage Using AI-Powered Data Discovery on Databricks
· 10 min read
DecisionBox autonomously analyzed 11 tables, executed 233 SQL queries, and surfaced 67 validated insights in 76 minutes — no dashboards to build, no SQL to write.
TL;DR
We pointed DecisionBox at a Databricks SQL Warehouse connected to a vacation rental dataset (WanderBricks — 11 tables, 500K+ rows) and hit Run Discovery. Seventy-six minutes later, the AI agent had:
- Explored every table autonomously, writing and executing 92 SQL queries during exploration alone
- Identified 67 insights across 7 analysis areas — 17 critical, 18 high severity
- Validated each claim with independent SQL queries — 21 confirmed exactly, 12 adjusted with corrected numbers
- Discovered a $5.8M revenue leak from cancellations, a 28.8% cancellation spike driven by same-day bookings, and an 80% host onboarding failure rate
No one told it what to look for. No one wrote a single query. The agent figured it all out.
