Meet your AI Agent for Policy Renewal Handling.

Cross-format extraction

InsurGPT™ parses loss runs across hundreds of carrier formats — structured PDFs, scanned images, spreadsheets with merged cells — pulling reserves, total incurred, recoveries, and claim counts with 98%+ accuracy.

Loss-Run Reconciliation | Cross-checked

The same loss reported in two carrier formats gets matched, deduplicated, and reconciled before it reaches your queue — no double-counting, no missed totals.

Exposure History | Sequenced

Builds a unified view of how payroll, vehicles, locations, and revenue have moved across renewal periods — the underwriter sees the trend, not just the snapshot.

Missing-Data Detection | Flagged

If MVRs are stale, payroll is incomplete, or financials are missing, the Agent flags the gap and requests it from the broker before the renewal hits the underwriter's desk.

Grounded, never invented

Every fact in the renewal brief traces back to the source document. X-Ray Mode highlights the exact page and field so underwriters verify in seconds, not minutes — and nothing reaches the file that the model can't show its work on.

X-Ray Mode | Traceable

Click any figure in the renewal brief and jump straight to the exact page, line, and coordinates on the original loss run or schedule where it was sourced.

Field-Level Citations | Cited

Every extracted value — reserves, total incurred, payroll, vehicle count — carries its origin: page number, line, and X/Y position on the document.

Source Document Linking | Anchored

The renewal brief stays anchored to the underlying file. Open any data point in context, side-by-side with the carrier loss run or broker submission it came from.

Calibrated confidence

Every extracted value carries a confidence score. Set your threshold — above auto-processes, below routes to a human reviewer. The model gets sharper on your book over time.

Confidence Thresholds | Calibrated

Every fact ships with a calibrated confidence score. Set the bar where you want it: above auto-processes, below routes to a human reviewer.

Human-in-the-Loop Routing | Routed

Low-confidence values are routed to a reviewer with the source document already open to the right page — verification takes seconds, not a file dive.

Continuous Learning | Sharpening

Every reviewer correction feeds back into the model. The Agent gets sharper on your carrier formats, your book, and your edge cases over time.