The context
- -Agriculture absorbs climate risk that no one can price: rainfall deficits, crop stress, and yield shocks are field-level events — but the data reaching financial institutions has always been regional, lagging, and too coarse to drive individual lending or insurance decisions.
- -Farmers are creditworthy but unmeasured: they don’t lack repayment intent — they lack a data trail. Without farm-level evidence, lenders deny credit and insurers price coverage out of reach, leaving smallholders exposed and disconnected from the capital they need.
- -AI-processed satellite data is the missing layer: near-daily satellite monitoring, fed through machine learning models, resolves farm-level vegetation stress, yield forecasts, and climate triggers at the granularity that financial institutions need to extend credit and parametric insurance — and that farmers need to finally access both.
Customer pain points
Grower
- Needed to monitor their fields regularly, but maintaining soil sensors was costly and complex. They also lacked tools to anticipate or respond to adverse weather events, or to plan irrigation with data.
Financial institution
- Assessed agricultural risk without consistent project data, making precise credit decisions and portfolio risk management difficult.
The value proposition
Grower
- The product makes field conditions visible through satellite monitoring and climate data, without the need for physical soil sensors.
Financial institution
- The product surfaces agricultural risk utilities and data at the field-plot level, with full portfolio visualization.
Finance Dashboard (interactive demo)
The outcome
Grower
- Enables regular field monitoring without maintaining soil sensors, taking action on adverse weather events, and planning irrigation with reliable information.
Financial institution
- Enables precise assessment of an agricultural project’s risk and visualization of aggregated risk across a portfolio of fields, facilitating more informed credit decisions and portfolio risk management.