Terrafuse develops AI-driven climate intelligence tools that analyze environmental data to generate precise climate risk assessments for businesses and governments. This technology enables users to make informed decisions regarding climate change impacts, enhancing resilience and strategic planning.
Funding
$750K 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.
Founders
Product
Problem
Businesses and governments face increasing challenges in accurately assessing and mitigating climate-related risks, such as extreme weather events and supply chain disruptions. Traditional climate risk assessments often lack the precision and granularity needed for effective strategic planning and resilience enhancement.
Solution
Terrafuse provides AI-driven climate intelligence tools that analyze extensive environmental datasets to generate precise, hyperlocal climate risk assessments. The platform leverages machine learning models trained on billions of earth observation data points to predict the likelihood of climate-induced damage at ultra-fine spatial resolutions. By embedding these risk scores into existing systems via API, users can make informed decisions to prevent losses and improve decision-making accuracy. The technology considers a wide range of environmental conditions and incorporates atmospheric physics to ensure highly accurate predictions validated against historical damage data.
Target Audience
The primary audience includes businesses and governments seeking to enhance their resilience and strategic planning through precise climate risk assessments.
Features
- Hyperlocal risk scores, ranging from 1 to 10, for any location
- Climate risk assessment at spatial resolutions of a few meters
- AI models trained on billions of unique earth observation data points
- Considers over 50 environmental conditions, including building density, fuel moisture, and vegetation
- Integrates atmospheric physics into machine learning models
- API integration for embedding risk scores into existing systems
- Validation against historical damage data to ensure prediction accuracy