LLM-based intake assistant that reads emails, extracts claim data, checks policy context, and drafts structured summaries for human review.
The client's claims team manually parsed inbound emails, PDFs, and scanned forms — losing hours per claim and producing inconsistent summaries that slowed adjuster decisions.
Illustrative screens — actual client UI, branding, and data redacted under NDA.
The intake agent uses GPT-4 class models for extraction with a smaller fast model for classification. Senior engineers designed the schema, evaluation harness, and fallback rules — the LLM is the worker, not the architect.
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