- Type
- not stated
- Format
- Tabular per-field feature vectors and time series keyed to field geometry, derived from optical and radar satellite imagery plus weather data; delivered as an analytics and API layer to insurer and lender systems rather than as raw imagery
- Volume
- Order of 100M-1B field-date observation records. Basis: near-daily revisit, roughly 200-300 observations per field per year, multiplied by the field count across a multi-country monitored footprint — but constrained by a company at a $1-10M revenue band and 11-50 employees, which caps area under active commercial monitoring well below continental wall-to-wall coverage. The ~35-variable feature vector per field per observation makes each record unusually wide
- Users
- N/A in consumer terms — a B2B analytics and API vendor whose customers are insurers, lenders, asset managers and agribusinesses. Institutional client base est. low dozens (inferred from 11-50 employees, a $1-10M revenue band, and a partnership model where the client carries underwriting and claims)
- History
- not stated
- Update frequency
- not stated
- Growth
- Active — AI Modeling v2 launched in 2025 for more accurate in-season yield prediction, leadership expanded with a new chief executive and chief technology officer in January 2025 plus a sales-director hire, the company exhibited at 10 industry conferences globally in 2025, and it was named Agricultural Insurance Data Provider of the Year 2026
- Launched
- ~2014-2016 founding, with corporate profiles disagreeing between 2014 and 2016, spun out of University of Wisconsin-Madison remote-sensing research by Dr. Mutlu Özdogan; the commercial satellite insurance product reached paying insurers est. 2017-2019 after the seed rounds
- Delivery
- not stated
- Entity mapping
- not stated
- Sample
- not stated
- Point-in-time
- not stated
- Licence
- not stated