For banks and lenders

Lend to the field, not just the file.

Agricultural credit is held back by thin evidence: what was sown, how it is growing, what the weather did. ai krishi turns satellite, weather and field data into a farm-level picture that lenders can appraise, monitor and plan portfolios around.

Request a briefing See the AI engine

The problem

Where agricultural lending struggles

Appraisal

What goes wrong today

Loans are sized on land and norms, with little evidence of how the field is actually performing
Verification of cropping pattern is manual and slow
Small farmers without records are excluded or pay more

What ai krishi does

Verified cropping pattern and crop-health history from satellite series
Land records read by the AI and confirmed by the farmer, not re-typed
Field-level weather exposure to support sizing and tenor

Monitoring between disbursal and repayment

What goes wrong today

Little visibility of the financed field until repayment is due
Stress is discovered when the instalment is missed
Field visits are spread thin and unprioritised

What ai krishi does

Seasonal crop-health tracking of financed fields
Early alerts when stress or weather events appear
A ranked list of accounts that need outreach first

Portfolio and climate risk

What goes wrong today

A drought or flood hits many accounts at once and is seen late
No district or crop concentration view across the book
Provisioning follows the loss instead of anticipating it

What ai krishi does

Portfolio heat maps by district, crop and season
Early-warning signals for planning outreach and provisioning
Scenario views of weather exposure across the book

Relief, restructuring and recovery

What goes wrong today

Blanket decisions after a disaster are unfair and costly
Evidence of actual loss is hard to assemble
Genuine borrowers and strategic defaulters look the same

What ai krishi does

Field-level evidence of loss to support targeted relief and restructuring
Shared evidence with insurers and officers, so claims and credit move together
Better-documented decisions for audit and review

Capabilities

What the lender gets

Verified cropping pattern

What is sown and how it is growing, from satellite history and farmer records.

Weather exposure

Field-level rainfall and event history, and forecast risk.

Early-warning alerts

Stress signals on financed fields before the instalment date.

Portfolio heat maps

Exposure by district, crop and season across the book.

Producer-organisation view

Credit and risk views for the whole organisation, from member fields.

Consent-based sharing

Our design principle is that a farmer’s field data reaches a lender only for a stated purpose and with the farmer’s consent. Arrangements are agreed per engagement.

What ai krishi does and does not do. It supplies farm-level evidence and early warning. It does not make lending decisions and it is not a credit rating. Appraisal, sanction and recovery stay with the lender and its policies. Connections to a lender’s systems are scoped for each engagement through open APIs.

Pilot

The case for starting small

Pick one branch cluster or one producer organisation and one season. Compare appraisal time, early-stress detection and delinquency in the pilot group against a matched group, and let the numbers decide whether to scale.

Request a briefing For producer organisations

See your book through the field.

A short briefing, then a single-season pilot with a clear baseline.

Request a briefing For insurers