Backed by Y Combinator

AI that knows the regs
better than your best adjuster.

Whether you're adjusting in the field or processing at HQ, Veltha checks every claim against the actual regulation, with citations, before errors become audit findings. MPCI, crop-hail, named peril, prevented planting.

Book a 15-minute call See how it works ↓

The problem

This is what claims season looks like today

Manual intakeAdjuster keys data into the claim system, uploads tickets, delivery sheets, storage measurements. Routes to the office.
Line-by-line reviewClaim hits threshold? Processor opens docs on one screen, claim data on the other. Compares line by line. Miskeyed test weight? Missing ticket? They catch it manually, or they don't.
Below threshold, no reviewUnder threshold? Nobody looks at it. Accuracy issues stay hidden until RMA edits flag an error.
PP stacks on more checksPP claims add 10-20 PPSH checks on top: planting history, double-cropping eligibility, acreage caps, NOL deadlines. All manual.
Peak season backlogPeak season hits. Claims compress into weeks. The backlog grows. Payments lag.

The platform

The only platform that checks claims against the regulation automatically

Compliance AI

Checks every claim against FCIC regulations, RMA standards, and company handbooks. Flags issues with paragraph-level citations. Handles the full range: MPCI yield claims, quality adjustments, prevented planting verification, crop-hail, named peril. Every decision has an audit trail.

Claims Automation

Extracts data from adjuster worksheets, scanned forms, and handwritten documents. Eliminates manual entry.

Risk Analytics

Surfaces discrepancies, outliers, and fraud indicators before authorities do.

How it works

Step 1

Intake

Connect your claims system or upload adjuster worksheets, photos, and scanned forms.

Step 2

Extract

AI reads every document (PDFs, handwritten notes, FSA-578 forms) and structures the data.

Step 3

Check

AI agents check claims against FCIC regulations, RMA standards, and your company handbook, flagging issues like incorrect quality adjustments or incomplete PP documentation with paragraph-level citations.

Step 4

Approve

Processors review flagged items with full context and citations. Approve or override with confidence.

The difference

No one else does this

What exists today

What Veltha does

Extracts data from documents

Extracts AND checks against the regulation

Requires human compliance review after

Runs compliance checks automatically with citations

Generic AI, doesn't know FCIC rules

Built for federal crop insurance: MPCI, crop-hail, PP

Flags “anomalies”

Flags specific violations with LAM paragraph references

Months of configuration

Upload documents, start processing

Built by engineers who've encoded regulation into software before

Ex-Palantir engineers who built systems that turn complex regulation into executable AI rules for government and enterprise. Now applying the same approach to federal crop insurance, encoding FCIC standards so machines can check compliance automatically.

Edin Citaku

Co-founder

Roland Jaloszynski