ai krishi

For insurers

Evidence for every claim, before the dispute.

Crop insurance runs on two things that are hard to get: knowing what was sown and knowing what was lost. ai krishi gives underwriters and claims teams field-level evidence, so assessment is faster, more objective and easier to defend.

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The problem

Where crop insurance loses time and trust

Underwriting and portfolio risk

What goes wrong today

  • Risk is priced on area averages, not on the field
  • Little visibility of what is sown where
  • Concentration of exposure in one district or crop is seen late

What ai krishi does

  • Field-level crop-health history and weather exposure
  • Verified sown crop and area from satellite series and farmer records
  • Portfolio heat maps by district, crop and season

Loss notice and claim assessment

What goes wrong today

  • Surveys are slow, so assessment lags the loss
  • Extent of loss is disputed because evidence is thin
  • Teams visit fields in the order complaints arrive, not by severity

What ai krishi does

  • Satellite crop-health deviation against the field’s own history and its neighbours
  • Rainfall departure and weather-event records for the exact location
  • Geo-tagged farmer photos as supporting evidence
  • A prioritised assessment queue, most affected areas first

Fraud, error and basis risk

What goes wrong today

  • Claims may not match what was actually sown
  • Area-level indices miss field-level reality
  • Genuine losers are under-paid and others over-paid

What ai krishi does

  • Cross-check of the crop claimed against crop-health evidence
  • Anomalies flagged for a human adjuster to review
  • A field-level evidence trail for every decision

Settlement and the farmer’s experience

What goes wrong today

  • Farmers cannot see where their claim stands
  • Delays push families into distress borrowing
  • Officers and insurers hold different versions of the facts

What ai krishi does

  • Farmers see status in plain language, on their phone
  • One shared evidence view for officers, insurers and farmers
  • Faster, better-documented settlement

Capabilities

What the insurer gets

Field-level evidence

Satellite crop-health history for every insured field, over the whole season.

Weather event records

Rainfall departure and alerts by exact location and date.

Photo evidence

Geo-tagged crop photos submitted by farmers and field staff.

Portfolio view

Exposure and risk by district, crop and season.

Triage for adjusters

A ranked queue so the most affected fields are assessed first.

Audit trail

Every score and flag shows the evidence behind it.

What ai krishi does and does not do. It supplies evidence and triage. It does not settle claims. Loss-adjuster judgement, the policy terms and the applicable official rules decide every claim. Connections to an insurer’s own systems are scoped for each engagement through open APIs, and we do not claim live integrations that are not in place.

Impact

Value, stated carefully

The impact model values faster, more objective assessment at 6% of annual claims paid, a planning assumption you can inspect and challenge on the Impact page. We recommend a baselined pilot: compare assessment time, dispute rate and loss ratio in treated and control areas.

Open the impact model

Test it on one portfolio.

Pick a district and a season. We will show you what the evidence looks like against your own claims history.

Request a briefingFor banks and lenders