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Work — Document & Data Processing

PolicyIQ

An insurance policy comparison AI for an independent insurance agency — carrier PDFs read into a structured ontology and compared side by side.

Status — In buildYear — 2026

The brief

Commercial insurance comparison runs on PDFs. Coverages, limits, exclusions, scattered across carrier documents in formats that don't match and language that doesn't reconcile. For an independent insurance agency handling submissions across multiple carriers, that reconciliation is the job—slow, repetitive, dependent on how closely each document gets read on a given afternoon.

PolicyIQ reads the documents. Carrier PDFs in; coverages, limits, and exclusions out, mapped to a structured ontology common across carriers regardless of who wrote the policy. Policies compared side by side, carrier against carrier, term against term. Every extracted claim carries an audit trail back to its source passage—no comparison asserts what the document doesn't say. Human-review checkpoints hold the line between extraction and decision. Built for operators and growing businesses; here, an independent insurance agency working commercial policy volume.

Two layers under the architecture: an LLM extraction layer, an LLM comparison layer. Cost-modeled at roughly $200 to $350 a month for a volume of 75 to 100 policies. The design is complete—twelve sections, adversarially reviewed before any production build. PolicyIQ: currently in build.

How it runs

The structure of the build, end to end — each stage hands off to the next.

01Intakecommercial policy PDFs arrive from multiple carriers
02ExtractionAI reads coverages, limits and exclusions out of each documentLLM extraction
03Ontologymismatched carrier language normalized into one structure
04Comparisonpolicies lined up side by side, gap by gap
05Audit trailevery extracted value traceable back to its source page
06Reviewhuman checkpoints before anything reaches a client

Delivered

Extraction pipeline: coverages, limits, and exclusions read from carrier PDFs into a structured ontology

Cross-carrier comparison: policies aligned side by side, term against term

Audit trail: every extracted claim traceable to its source passage

Human-review checkpoints placed between extraction and decision

Twelve-section technical design, adversarially reviewed

Cost model: roughly $200–350/month at 75–100 policies/month volume

LLM extraction layerLLM comparison layerStructured ontologyAudit trailHuman-in-the-loop review

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