Meet your AI Agent for FNOL / FROI Setup

Trained on insurance, not the internet

InsurGPT™ isn't a general-purpose model with an insurance prompt taped to the front. It's an ensemble of specialized AI models trained on 300M+ non-public insurance documents — ACORD forms, FROI/SROI, demand letters, police reports, mailroom scans — across dozens of carrier-specific formats.

Domain-Native Corpus | Insurance-only

300M+ non-public insurance documents in the training set — ACORD forms, FROI/SROI, demand letters, police reports, photos, and mailroom scans — not scraped web text dressed up for the industry.

Ensemble of Specialists | Multi-model

Dozens of specialized models, each tuned for a specific document type and extraction task. The right model reads the right artifact — not one generalist guessing at all of them.

Field-Level Confidence | Scored

Every extracted value ships with a calibrated confidence score so you know which fields are safe to auto-process and which deserve a second set of eyes.

Verifiable, not opaque

Black-box outputs don't survive an audit, a regulator, or a coverage dispute. X-Ray Mode shows the source document behind every value the model extracted — page, paragraph, coordinates. Auditable. Defensible. Reviewable in seconds.

X-Ray Mode | Traceable

Click any extracted value and jump straight to the exact page, paragraph, and X/Y coordinates in the source document where it came from — no scrolling through 40-page PDFs to verify.

Audit-Ready Trail | Defensible

Every decision, every extraction, every confidence score is logged with its source. When a regulator, auditor, or opposing counsel asks how a value was derived, the answer is one click away.

Human-in-the-Loop Routing | Reviewable

Confidence below threshold? The field routes to a human reviewer with the source context already attached. Above threshold? Straight-through to the claim system.

Continuously improving

Static models go stale. InsurGPT™ uses federated learning across every customer deployment — performance compounds over time as new document formats, edge cases, and carrier-specific quirks flow back into the ensemble.

Federated Learning | Networked

Every customer deployment contributes to model improvement without exposing private claim data. The network gets smarter together — your data stays yours.

Edge-Case Capture | Compounding

Unusual document formats, malformed forms, carrier-specific quirks — each one seen in production becomes training signal. Accuracy on the long tail goes up every quarter.

Production Feedback Loop | Live

Adjuster corrections in X-Ray Mode feed directly back into model tuning. The model you deploy on day one is not the model you'll be running in month six.