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.
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.
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.
FNOL hits a Custom HTTP tool that routes payload + attachments into the Network.
The built-in ocr tool plus an extraction agent turn images into typed fields with provenance.
Policy documents are vectorized. The Coverage Agent finds the right clauses, cites them, and feeds them to the rule engine.
The Payout Agent emits a decision; the Network routes by authority threshold to auto-approve, adjuster, or fraud team.
Every claim's run is replayable. SEEMR optimizes which model handles which sub-task.

average cycle time on routine claims.
evidence packs include OCR provenance + policy citations.
fraud team focus on real signals, not data wrangling.
SEEMR learns which sub-intent (extraction, coverage, fraud) maps to which model, balancing cost, latency, and accuracy.
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.