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An internal prototype exploring retrieval-augmented generation (RAG) to synthesize unstructured medical charts into structured clinical summaries for medical practitioners.
Clinicians face hours of daily administrative fatigue manually reviewing decades of non-standardized EHR records, PDFs, and lab notes before patient consultations.
We engineered an end-to-end local-first RAG pipeline using LangChain, pgvector, and Next.js. The system securely ingests medical records, indexes clinical entities with hybrid search, and generates sourced summaries with precise page-level citations.
Our engineering leads will provide architecture feedback and a structured development roadmap.