Completed
Closed. Second regional insurer now licensing the model.
FoxfireThis project has been verified
Wildfire risk prediction for insurers, built on satellite and weather data, not historical averages.
$750,000 raised so far, confirmed by both parties (83%)
Details
- Category
- AI / Machine Learning
- IP type
- Software
- Stage
- Revenue
- Ask type
- Fixed amount
- Listed
- Sep 16, 2026
- Views
- 1
Description
Foxfire models wildfire risk at the individual-property level using live satellite vegetation data, weather patterns, and terrain, giving insurers a far more precise pricing signal than the county-level historical averages most still rely on.
Problem
Most wildfire risk pricing still relies on broad historical averages, badly mispricing risk at the level of an individual property.
Solution
Property-level risk modeling using live satellite vegetation and weather data combined with terrain analysis, updated continuously rather than recalculated annually.
Target market
Property and casualty insurers in wildfire-prone regions.
Business model
Annual data licensing contract with insurers.
Investments
Oliver Schmidt invested $500,000
8% equity
Freya Nilsen invested $250,000
4% equity
Discussion (1)
Oliver Schmidt
Property-level risk instead of county-level averages is a meaningfully better pricing signal for insurers.
Chloe Bennett
Innovator·🇬🇧 United Kingdom
Climate risk analyst, previously modeled catastrophe risk for a reinsurer.