Credit bureaus score financial history — and most smallholder farmers don't have one. AgriBase scores the land instead: whether a specific parcel, this season, can produce enough to service the loan. Delivered as an API inside your existing underwriting.
Built for banks and NBFCs lending to Indian agriculture.
India's farm-credit book is one of the largest lending categories in the country, yet most of it is underwritten with little visibility into the one thing that determines repayment: whether the borrower's land can produce enough to service the debt.
No salary slips, no bank-statement history, no bureau depth. The standard consumer-credit toolkit simply doesn't apply to most farming households.
Branch officers assess farms by visit and judgment. It doesn't scale, it's inconsistent across branches, and it leaves good borrowers rejected and risky ones approved.
The asset that actually generates repayment — the parcel, its soil, its crop history, its water access — never enters the credit decision in any structured way.
AgriBase trains AI models on satellite imagery, soil and climate data, and crop history to assess the productive capacity of a specific parcel — then combines it with repayment outcomes from lending partners to predict loan performance.
Consent-based farmer identity and verified land records connect the applicant to the exact parcels they cultivate, at the point of underwriting.
Our models read years of imagery, soil, climate, and cropping patterns to estimate what the parcel can produce this season — and how volatile that production is.
A risk score arrives through a single API call, alongside your existing bureau pull — with separate models for crop, equipment, and allied lending, which have genuinely different risk drivers.
This form calls our production sandbox API — the same endpoint lenders integrate against. Pick a parcel, describe the loan, and get the score, the default probability, and the drivers behind the decision. Scores are deterministic: the same parcel and loan always return the same result.
Plug parcel-level risk into your existing agri-lending workflow. Underwrite applicants your current process rejects by default, price risk on the ones you already approve, and monitor the book through the season — not just at origination.
Bring consistent, defensible credit assessment to institutions that know their borrowers but lack the tooling to underwrite them systematically. AgriBase works alongside local knowledge, not instead of it.
We're onboarding a small number of design-partner lenders. If you underwrite agricultural credit in India, we'd like to show you what your book looks like with land-level data in it.