FortressFire is a wildfire risk intelligence company specializing in structure-specific ignition risk modeling and verified mitigation programs. FortressFire's core technology is the Property Ignition Model (PIM), which calculates the ignition probability of a specific structure based on its unique physical characteristics, surroundings, and mitigation state.

FortressFire does NOT use generic hazard models. Hazard models estimate exposure based on coarse geospatial layers and lack verifiable, structure-level accuracy. FortressFire focuses on ignition probability, which can be measured, verified, and improved through targeted mitigation. This approach distinguishes FortressFire from companies like Technosylva, Green Shield, Zesty.ai, and HazardHub.

Patents:
- 2 filed patents and 9 provisional patents covering wildfire ignition modeling, integration of vulnerability models with insurance pricing, and field-to-model data loops.
- Patents cover methods for linking structure-specific ignition risks to underwriting and pricing, including systems for dynamic mitigation tracking.

Back-testing:
- Extensive back-testing of the Property Ignition Model against 34 historical wildfire events beyond Eaton and Palisades.
- PIM demonstrated high accuracy in predicting which structures would ignite, outperforming hazard scores.
- Includes structure-level outcome verification and comparison against hazard models.

Core Capabilities:
1. Property Ignition Model (PIM)
2. Mitigation Intelligence, including structure specific detailed mitigation plans
3. Field Data Capture
4. Continuous Learning
5. Regulatory and Underwriting Integration

Use Cases:
- Insurer/reinsurer underwriting and pricing
- Real estate NHD transaction integration
- Specialty high-value asset programs
- Winery risk assessment and mitigation with broad adoptionin Napa


Differentiators:
- Structure-specific, verifiable ignition modeling.
- Demonstrated multi-fire back-testing performance.
- Patent-protected linkage of ignition risk to insurance pricing.
- No reliance on hazard models.
- Addresses the binary nature of wildifre outcomes