Case study
Medical Document Entity Extraction
Multimodal LLM extraction that raised accuracy from 84% to 88% and cut manual review overhead by around 60%.
Problem
Medical and insurance teams spent hours extracting data from documents that mix printed forms, handwritten notes, and messy multi-page layouts. Critical fields were missed, and the previous system needed weekly fine-tuning just to keep up. Accuracy stuck at 84%.
The client needed a production pipeline that handles varied layouts without constant retraining, while pushing accuracy past 84% and reducing operational overhead.
Solution
We built a multimodal extraction pipeline using LLMs (OpenAI, QWEN3) orchestrated with LangGraph. The system ingests document images and layout metadata, runs structured extraction with fallbacks for low-confidence regions, and resolves multi-page entities consistently.
Output passes through normalization and validation gates before downstream systems consume it. Bad extractions are caught in the pipeline, not in production databases.
Outcomes
Extraction accuracy improved from 84% to 88%.
Weekly fine-tuning cycles eliminated; manual review time dropped by around 60%.
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