Agentic Claims Intake & Triage
Upload a claim, watch the agent extract entities, run a grounded coverage lookup, cross-check for fraud, and escalate to a human when confidence drops.
A claim arrives as a document. Normally an adjuster reads it, keys the details into the claims system, checks coverage by hand, and decides where it goes.
This pipeline runs those steps as one flow: n8n parses the document, an LLM extracts entities against a fixed schema, a grounded lookup checks coverage, and a fraud cross-check scores the claim.
When the confidence score drops below threshold, the claim escalates to a human instead of auto-deciding. Every step writes to an audit trail, so any routing decision traces back to the evidence that produced it.
How it's wired
- 01PresentationNext.js on Vercel
Claim upload, live status, and rendered payload for the demo.
- 02Orchestrationn8n (cloud-hosted)
Document parsing, coverage lookup, fraud cross-check, and the human-in-the-loop escalation gate.
- 03DataTODO— Object storage + routing queues
Claim documents, extraction records, routing queues, and the audit log.
- 04AIOpenAI API (schema-validated calls)
Entity extraction, grounded coverage lookup, and fraud scoring.
Claim uploaded on the demo page → n8n webhook receives the file reference → document parsed and entities extracted against a fixed schema → grounded coverage lookup runs → fraud cross-check scores the claim → confidence below threshold escalates to a human, otherwise routes to the correct queue → audit record written, structured result returned.
What goes in, what comes out
Mock data — representative of the production schema.
{
"event": "claim.received",
"submitted_at": "2026-07-05T14:12:00Z",
"source": "claims.elwoodberry.com",
"fields": {
"filename": "auto-claim-4471.pdf",
"pages": 12,
"policy_ref": "MOCK-2026-0091"
}
}{
"status": "triaged",
"confidence": 0.91,
"routed_to": "queue:adjuster-review",
"audit_id": "ias-demo-001-0001"
}Want this for your team?
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