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The AI model automatically identifies and populates every critical policy field the moment a document arrives, virtually eliminating the need for staff to perform repetitive data entry.
By replacing manual typing with high-precision extraction, the system removes the risk of typos in coverage limits and VINs, preventing costly downstream endorsements and disputes.
Metrics and logs flow seamlessly to CloudWatch, ELK, and Datadog with CloudWatch Alarms routing priority alerts directly to Slack, cutting detection-to-action time by 40%.
The transition from weeks to days for onboarding new event types allows the underwriting team to adapt to market changes and new policy formats with unprecedented speed.
The client, a prominent auto insurer operating across a six-state footprint in New England, faced significant operational friction due to manual documentation workflows. Three core pain points emerged:
Critical data was trapped in static PDFs and scanned forms. Staff were forced to manually retype names, VINs, and coverage details into core systems, creating a massive administrative bottleneck.
The manual nature of the workflow meant that quote packages often took hours to reach agents and brokers, leading to lost opportunities in a highly competitive market.
Frequent typos in coverage limits and vehicle details triggered a cycle of costly endorsements and preventable claim disputes, damaging both the bottom line and brand reputation.
With over 150 underwriting and claims professionals tied up in data entry, the organization lacked the bandwidth to focus on complex risk assessment and high-value client service
Inferenz implemented an end-to-end AI extraction layer that converts unstructured insurance documents into clean, validated data, dropping it directly into the client’s core policy-administration systems:
Event-Driven Ingestion
Deployed an Azure-native architecture where every file upload triggers an immediate processing event. This ensures that policy data begins moving through the pipeline without any manual “hops.”
Custom AI Form Recognition
We built and trained a custom model tailored specifically to the client’s insurance forms. The system captures names, addresses, vehicle details, and complex coverage grids in seconds with high confidence scores.
Advanced Python Post-Processing
A dedicated transformation layer using Pandas, Regex, and NumPy tidies the raw AI output. This stage formats addresses, validates VIN checksums, and builds a perfectly structured JSON record.
Secure API & Serverless Integration
Using Azure API Management, the system provides a secure gateway for JSON data to flow into quoting tools. The serverless design means the insurer pays only for what they use, with capacity scaling automatically during high-volume months.
Manual documentation effort was cut by more than three-quarters across the entire underwriting department.
Precision extraction ensured audit-ready records and drastically reduced the need for mid-term policy corrections.
Quote-to-bind speed moved from hours to minutes, significantly increasing broker satisfaction and hit ratios.
The cloud-native, serverless approach removed the need for internal server management while providing 24/7 availability.
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