
Insurance & Underwriting
The strongest predictor of food safety incidents exists. It's just locked inside hundreds of different city databases.
When you're writing food-safety liability policies, actual inspection outcomes predict risk exposure better than any proxy. But that data is scattered across hundreds of jurisdictions with different schemas. We aggregated and normalized it all, cross-referenced it with 311 pest complaints, and deliver it in bulk.
Sound familiar?
Inspection history predicts claims better than location, revenue, or cuisine type. But no insurer has the engineering team to aggregate it from hundreds of city databases.
NYC violation codes don't match Chicago's. Houston's grading system doesn't map to Boston's. Without normalization, the data is unusable at scale.
When you do pull inspection data for a specific applicant, it's a manual process. Weeks of turnaround for one restaurant. Doesn't scale to a book of 10,000.
Structured inspection data from 30+ jurisdictions, cross-referenced with 311 pest complaints, delivered in bulk for your actuarial models.
Transparent pricing. No hidden fees. Cancel anytime.
Sample dataset to test signal quality
For regional underwriting models
Full national dataset
âWe've been looking for a structured, multi-jurisdiction inspection data feed for years. Every insurer knows this data predicts claims, but nobody had it aggregated and normalized until now.â
Start with a 3-jurisdiction sample. If the data improves your loss ratio model, we'll scale to national. The evaluation cost credits toward your annual license.