
Terrafuse AI provides hyperlocal climate risk intelligence powered by machine learning. Their solution offers risk scores from 1 to 10 for any location at resolutions of a few meters, utilizing AI trained on billions of Earth observational data points. The process involves data sourcing from the scientific community, training models on environmental conditions and loss severity, validation against historical data for accuracy, and embedding risk scores into customer systems via API for climate-informed decision-making. Founded as a spin-out from Lawrence Berkeley National Laboratory, Terrafuse aims to embed AI-driven climate risk insights into decision-making across various industries.

Terrafuse AI provides hyperlocal climate risk intelligence powered by machine learning. Their solution offers risk scores from 1 to 10 for any location at resolutions of a few meters, utilizing AI trained on billions of Earth observational data points. The process involves data sourcing from the scientific community, training models on environmental conditions and loss severity, validation against historical data for accuracy, and embedding risk scores into customer systems via API for climate-informed decision-making. Founded as a spin-out from Lawrence Berkeley National Laboratory, Terrafuse aims to embed AI-driven climate risk insights into decision-making across various industries.
What they do: Hyperlocal climate risk intelligence using ML to produce location-level risk scores
Founding: Spin-out from Lawrence Berkeley National Laboratory; founded 2018
Team size (approx.): 8 employees
Funding: Primarily grant funding; reported total ≈ $990k (USD)
Climate risk intelligence; hyperlocal weather and hydroclimate risk modeling
2018
Climate tech / AI
990000.00
Reported grant funding; company has multiple grant rounds and NSF-listed as a lead funder
“Grant-focused funding with National Science Foundation and Cleantech Open listed as investors”