$15.6 M
in annual revenue
30k
insurance clients
42
years in operation
The client’s underwriting staff at retyped every policy by hand, including names, VINs, coverage limits, from scanned PDFs into core systems, with no automated bridge between the two. Inferenz built an AI-driven extraction layer on Azure Form Recognizer, trained a custom model to capture complex policy fields, and connected it to the insurer’s quoting tools through a secure API. Underwriters recovered 80% of the time once spent on manual entry, and the insurer now processes policies with zero re-keying and audit-ready accuracy.
The client's entire underwriting operation rested on manual re-keying across a six-state footprint, with no automated bridge between incoming documents and core systems, unable to support the volume and speed the market demanded.
Underwriters manually retyped names, VINs, coverage limits, and addresses from scanned PDFs into core systems for every single policy. Every new file added to the backlog.
Quote packages could take hours to reach agents and brokers after a document was uploaded, with each field checked and entered independently.
A single mistyped VIN or coverage limit could trigger a costly endorsement or claim dispute months later, a routine cost of manual entry at this scale.
Several underwriting and claims professionals spent their days keying data instead of assessing risk.
Inferenz built an end-to-end AI extraction layer on Azure, converting unstructured insurance paperwork into clean, validated data flowing directly into the client's policy-administration systems. Here's what they did:
Azure Functions trigger processing the instant a file is uploaded, replacing fragmented manual handoffs with a live pipeline.
A Form Recognizer model trained on the insurer's actual forms captures names, addresses, vehicle details, and coverage grids in seconds — the prerequisite for zero-touch policy ingestion at scale.
A Pandas- and Regex-driven pipeline normalizes and validates every extracted field, catching errors before they reach a human.
Azure API Management streams structured JSON records directly into core quoting tools in real time.
The infrastructure scales automatically through peak renewal periods, with no added hardware and no servers to maintain.
Search Modalities in Production
Natural language + visual image search
Days Retraining Cycle
NER model retrained as new cameras deploy
Filter Configuration Required
Non-technical users search from day one
Search Coverage
Queries span every connected camera simultaneously
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