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We fuse Earth Observation data, heavy climatic modeling, and advanced Machine Learning to transform uncertainty into actionable metrics.
We capture the pulse of the planet using the most reliable constellations and ground networks.
Sentinel-2 (NDVI), Sentinel-1 (SAR) for crop health monitoring even through clouds.
GOES & Meteosat for real-time storm tracking and rapid fire detection.
ERA5 & CHIRPS providing 40+ years of historical consistency for risk pricing.
Local validation networks to calibrate satellite proxies against ground truth.
Raw data is noisy. Our AI layer cleans, reconstructs, and enhances data to achieve insurance-grade precision.
Using Deep Learning (GANs) to reconstruct missing optical data obscured by cloud cover, ensuring continuous monitoring.
Machine Learning algorithms calibrate large-scale weather models against local station data to reduce basis risk.
Proprietary AI models trained on decades of agronomic data to forecast crop yields and trigger parametric payouts accurately.
Computer vision automatically detects extreme events like hail scars or flood extent from radar imagery.
We aggregate over 200 forecast members from NOAA (GFS), ECMWF (IFS), and DWD (ICON) every 6 hours. This approach quantifies uncertainty and extends lead times by 12+ hours compared to deterministic models.
Every index and model is rigorously backtested against 30 years of historical data and validated by local agronomic institutes to ensure fair and transparent triggers.