PLAYBOOK · INSURANCE

An on-prem claims processing network from FNOL to settlement.

FNOL intake, document OCR, coverage check, fraud signal, payout decision — every stage is a sub-intent. This playbook orchestrates them as one VDF AI Network with end-to-end traceability.

OCRSemantic SearchRule AgentsApproval Routing
VDF Data Overview
The problem

Claims still move paper

Even digital claims arrive with scanned receipts, photos, policy PDFs, and free-text descriptions. Adjusters spend hours on data extraction before they get to the decision.

The VDF AI approach

One network, every stage

OCR built-in. Policy vectors in pgvector. Rule agents wired to your engine. Approval routing baked into the Network. A claim enters; an evidence-backed decision exits.

REFERENCE ARCHITECTURE

FNOL to settlement, one network

FNOL Intake
email · portal · API
OCR + Extraction
Policy & Coverage RAG
Custom HTTP Tool
Rule engine
Coverage Agent
Fraud Signal Agent
Payout Agent
Claims Network
Intent: process-claim
Decision + evidence pack
Adjuster review queue
PLAYBOOK · STEP BY STEP

From FNOL to evidence-backed decision

1

Ingest the claim

FNOL hits a Custom HTTP tool that routes payload + attachments into the Network.

2

OCR + structured extraction

The built-in ocr tool plus an extraction agent turn images into typed fields with provenance.

3

Coverage retrieval

Policy documents are vectorized. The Coverage Agent finds the right clauses, cites them, and feeds them to the rule engine.

4

Decision and approval routing

The Payout Agent emits a decision; the Network routes by authority threshold to auto-approve, adjuster, or fraud team.

5

Audit and learn

Every claim's run is replayable. SEEMR optimizes which model handles which sub-task.

Claims processing network live execution
OUTCOMES

Throughput and trust at the same time

−55%

average cycle time on routine claims.

100%

evidence packs include OCR provenance + policy citations.

fraud team focus on real signals, not data wrangling.

SEEMR REFERENCE

Adjuster time goes to real decisions

SEEMR learns which sub-intent (extraction, coverage, fraud) maps to which model, balancing cost, latency, and accuracy.

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GET IN TOUCH

You Have Questions

Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.