Kettle uses proprietary AI models to generate high‑resolution wildfire risk assessments, enabling precise pricing and coverage for property owners in high‑fire‑risk areas. It offers commercially underwritten excess‑and‑surplus property policies and parametric wildfire products, while also providing insurers and reinsurers with data‑driven underwriting and reinsurance capacity.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
6OFounders
Product
Problem
Traditional insurance underwriting struggles to accurately predict wildfire and other climate‑related catastrophes, leading to coverage gaps, inflated premiums, and frequent denial of policies for property owners in high‑risk regions.
Solution
Kettle applies proprietary machine‑learning models that ingest billions of weather, satellite, fuel and ground‑truth data points to generate high‑resolution wildfire risk assessments. By quantifying ignition probability, spread dynamics, building vulnerability and parcel‑level exposure, the platform can price coverage more precisely and identify mitigation actions that lower premiums. The company offers commercially underwritten excess‑and‑surplus (E&S) property policies and parametric wildfire products that provide full indemnity up to $200 M total insured value and rapid claim payouts within 15 days. Distribution is handled through approved wholesale partners, enabling insurers and reinsurers to access Kettle’s data‑driven risk insights for the California, Nevada and broader U.S. markets.
Target Audience
Primary customers are commercial property owners, homeowners associations and real‑estate portfolios in high‑fire‑risk regions, as well as insurers and reinsurers seeking AI‑enhanced underwriting and reinsurance capacity for wildfire exposure.
Features
- Proprietary AI pipeline that processes weather, fuel, satellite (NASA, NOAA, ESA) and anthropogenic data to produce over 2 million wildfire footprints at 100 m resolution
- Suite of deep neural network models (Ignition, Contagion, Building Vulnerability, Fire Edge Downscaling, Parcel Risk Assessment) delivering an 89.2 % accuracy score for ignition forecasting
- Micro‑grid risk mapping that divides California into 419 000 cells (0.5 sq mi each) for granular exposure analysis
- Parametric wildfire insurance (“Fire in a Parcel”) with automatic claim triggers and payouts within 15 days across the continental U.S.
- Commercial E&S property insurance covering up to $200 M total insured value with limits up to $10 M per risk for properties in California and Nevada
- Integration of mitigation scenario modeling that quantifies premium reductions for fire‑hardening measures