A first notice of loss arrives as whatever the claimant had to hand — a photo of a form, a scanned PDF, a phone note. FNOL turns that into a structured claim record with each field grounded back to the pixels it was read from.
Photos, scans and PDFs converted into structured, source-grounded claim data, validated and handed off to CRM automatically.
A first notice of loss arrives as whatever the claimant had to hand — a photo of a form, a scanned PDF, a phone note. FNOL turns that into a structured claim record with each field grounded back to the pixels it was read from.
Validation runs before hand-off: required fields, policy-in-force checks, obvious inconsistencies. A claim that fails validation is queued for a human, not silently accepted.
On success the record is pushed to CRM and the claims workflow with the evidence attached. The adjuster opens a claim that already has its documents indexed.
Python · Rust · Azure Content Understanding · Azure OpenAI · HubSpot API