Home A · peak local velocity
88 mph
Tree shelter reduces wall flow; velocity peaks over the roof
Property-level wind risk analytics
Sharper wind risk at the building level.
Downstream delivers hyperlocal wind intelligence on top of the catastrophe models the market already runs, helping underwriters see where building-level risk diverges from modelled expectation.
Property wind lab
100 mph wind
The wind is identical. Property characteristics change the expected loss.
Wind west to east
Home B · peak local velocity
118 mph
Flow accelerates above the freestream at the exposed eave
The problem
Cat models price hurricanes at a kilometre. Losses happen at a building.
Two properties one street apart can carry very different true wind risk. Roof form, elevation, tree cover, nearby structures, and channelled wind flow all matter, but those signals rarely fit cleanly into legacy exposure schemas.
~1 km
Typical native hazard resolution
850k+
Labelled buildings in public damage datasets
The solution
One multiplier. Zero change to your stack.
Downstream learns where observed damage diverges from modelled expectation, then delivers that difference per property.
Your model output
The AAL or loss estimate your licensed catastrophe model already produces for the risk.
Property-level wind-damage foundation model
Learns a latent damage signal from property imagery, geospatial context, weather history, and observed post-event damage.
Property-adjusted view
Same workflow, same model, sharper view: select, price, and steer at the building.
Underlying magic
We model the residual, not the storm.
Baseline first
A defensible fragility baseline establishes what the physics says should have happened.
Then the surprise
The model learns where reality diverged: construction, exposure, micro-terrain, vegetation, and built-environment signals the grid cannot see.
Rides on, never replaces
Output is an auditable multiplier on the model you already license, designed for adoption without a re-platforming decision.
Founding team background
Insurance, catastrophe science, and ML under one roof.
The founding team brings training and operating experience from leading research, aerospace, and risk analytics institutions.
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