Summary
- PerilScore is preparing free property peril scores for ChatGPT.
- Scores cover hurricane, wildfire, hail, flood, tornado, and earthquake risk for any US address.
- Legacy providers often charge $3-5+ per call with annual minimums; PerilScore is building a free entry point.
- The full platform adds building data, imagery, transparent methodology, and submission-ready PDFs with no annual minimums.
PerilScore is preparing property-level peril scores for ChatGPT. The goal is simple: let anyone ask for hurricane, wildfire, hail, flood, tornado, and earthquake risk context for a US address, without starting in an enterprise sales funnel. The live PerilScore app already supports free address previews today.
What This Means
The property risk data business has always been built on aggregation. Legacy vendors created value by pulling together storm data from NOAA, weather records from the National Weather Service, county assessor information, and dozens of other public sources into packaged scores that insurers could access through APIs. The difficulty of assembling and maintaining those data pipelines justified the pricing, and the enterprise sales model created enough friction that customers rarely switched once integrated.
The integration cost is falling. Public and licensed sources that once required years of custom data work can now be cross-referenced faster. Delivery can also start in consumer and agent workflows, not only behind API contracts.
PerilScore in ChatGPT is designed to give users a complete peril breakdown showing which factors drive each risk score. For insurance teams that need more, the full platform adds verified building construction data, aerial imagery analysis, building code evaluation, and submission-ready reports with no annual minimums.
Why Transparency Matters
Legacy peril scoring has historically operated without much visibility into methodology. You receive a score, but you cannot see the specific factors that produced it or how they were weighted. When a property scores poorly, there is no clear path to understanding which inputs are driving the assessment or whether better data might change the result. This opacity was not incidental; when customers cannot see how a score is calculated, they cannot easily replicate it or challenge it, which creates stickiness.
PerilScore takes the opposite approach. Every score comes with a breakdown of contributing factors. If a property has elevated hurricane risk, users can see how distance to coast, roof age, construction type, and local wind history each contribute to the assessment. If better data exists for any factor, users can override it and watch the score recalculate in real time.
The Larger Context
Property peril scoring is the first visible surface. Once property data is easier to package, the next question is what happens downstream: submission prep, underwriting review, capacity routing, and quote workflows. That is where PerilScore is headed.
What Happens Next
The ChatGPT release is in progress. In the meantime, the free property risk preview is live now at app.perilscore.com/free-score/, where you can enter any US address, preview risk instantly, then unlock complete building-level risk reports with no annual minimums.