Underwriters don't need another inbox. They need submissions broken into structured fields, checked against guidelines, and only escalated when something is incomplete or off-policy.
Broker packages arrive as PDFs, scanned ACORD forms, loss runs, and email threads. Someone opens the file, re-keys fields into the underwriting system, and decides whether the submission is complete enough to quote.
That triage is slow for two reasons. First, extraction is manual and inconsistent across desks. Second, completeness rules live in people's heads or a shared spreadsheet, so the same package can be accepted one day and kicked back the next.
When volume spikes, the bottleneck is not judgment. It is getting clean, checked data in front of the person who is allowed to use judgment.
We build an agent pipeline that ingests the submission package, extracts the fields underwriting actually needs, validates them against your guidelines, and produces a structured triage outcome: ready to quote, missing documents, or escalate to a human.
Extraction is only step one. Deterministic checks run after the model: required forms present, named insured consistent across pages, loss history within lookback windows, limits and classes allowed for the appetite matrix. Failures attach the evidence and land in a review queue—not a chat window.
Underwriters keep the decisions that require judgment. The system owns the re-keying, checklisting, and audit trail.
Sense: pull the package from email, portal, or DMS; split documents; classify ACORD vs loss run vs correspondence.
Reason: extract fields with a model under a fixed schema; run rule checks from your underwriting guidelines; score confidence per field and per package.
Rock: write structured data into your underwriting or rating system; open a human task when confidence is low or a hard rule fails; log every extraction, check result, and override with who changed what.
Scanned multi-page loss runs with tables the model misreads—without field-level confidence, bad numbers flow into rating.
Guidelines encoded only in prompts. When appetite changes, nobody can version or review the rules the way they review code.
No dual-control on high-severity lines. The system needs a hard stop that requires a senior underwriter before the package is marked quotable.
Silent retries on partial packages. Operators need a visible queue of "waiting on broker" with the exact missing items, not a dead thread in an agent log.
It is the process of taking a broker submission package, extracting the data underwriting needs, checking completeness against your guidelines, and deciding whether the package is ready to quote or needs human follow-up. Done well, it is an agent system with rules and review paths—not a chatbot that summarizes the PDF.
No. The agent replaces re-keying and checklist work. Appetite decisions, exceptions on complex risks, and anything outside the written guidelines stay with underwriters. The system should make those people faster, not invisible.
Hard rules and low-confidence fields create a structured exception. The package is marked incomplete, missing items are listed for the broker or CSR, and nothing is marked quotable until the gap is closed or a human overrides with a reason.
Whatever already owns the underwriting workflow: policy admin, rating, document management, email, or a custom intake portal. The agent is designed around your APIs and file formats, not a greenfield SaaS silo.
A useful first loop is one line of business, one package type, and a small set of completeness rules running on real submissions with a review queue. That is typically weeks for a scoped engagement—not a multi-year platform rewrite. Expand appetite rules and document types after the first loop is stable.
Engineering services
Insurance submission triage and data extraction is the kind of workflow we build as an AI agent system: deterministic checks, human checkpoints, and an audit trail operators can trust. We start from your real process boundaries and ship software that holds up in production.
We build production-ready, highly observable agentic systems engineered for enterprise scale. No black boxes, no magic—just systematized workflows with systemic safeguards.
We don't build fragile wrappers. Complex decisions and exceptions are automatically routed to your team for approval, ensuring zero unverified actions in production.
Every AI-generated output is validated against deterministic, programmatic rules before execution, guaranteeing structural integrity and compliance.
Our architecture records every state change, agent reasoning step, and user interaction, providing complete observability into your automated workflows.
Built for enterprise scale. We optimize for high-throughput, low-latency execution using edge infrastructure and efficient state management.
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