Inspiration
Insurance underwriting and claim review involves massive manual document reading, data extraction and rule checking. Reviewers spend lots of time repeating low-value work on messy application forms, medical records and photos. We hope to build an AI assistant to automate these repetitive tasks, cut processing time and reduce human error.
What we learned
We learned how to combine LLM, document OCR and business rule engines for domain-specific insurance workflows. We also gained experience balancing AI free-text understanding with strict fixed insurance compliance rules, and how to design reliable tool calling for structured data output.
How we built it
This project belongs to the Frankenstein category. We assembled multiple components: an OCR parser to read scanned documents, a large language model agent to extract key fields, a rule checker for insurance policy constraints, and a simple backend to aggregate results. All modules are connected through API calls to form an end-to-end review pipeline.
Challenges we faced
The biggest challenge was inconsistent document formats. Hand-filled forms, blurry scans and irregular attachments made information extraction unstable. Another difficulty was aligning AI outputs with rigid insurance rules — the model tended to give flexible explanations instead of deterministic compliance judgments. We solved it by adding post-processing rule validation layers to constrain the agent’s results.
Built With
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