Hospitals, clinics, and healthcare providers accumulate large volumes of paper-based and unstructured health records—scanned forms, lab reports, discharge summaries, handwritten notes, and legacy PDFs. These records are slow to search, easy to misplace, and difficult to extract meaningful data from. The result is friction for staff, delays in accessing patient history, and missed opportunities to use historical data reliably when care decisions need speed and clarity.
We deliver a production-grade health records digitization pipeline: a Dockerized system that combines OCR (optical character recognition) with an LLM-powered extraction layer. It ingests scanned/physical records, converts them into clean text, then automatically structures and cleans the information into consistent fields (patient identifiers, diagnoses, dates, medications, procedures, and more). The output is structured, searchable digital data—generated automatically, at scale, without manual data entry.
Built on a production-grade pipeline, tested end-to-end
pipeline turning scanned records into structured, searchable data
Scan or upload physical health records/documents.
OCR engine extracts raw text from the documents.
LLM layer structures and cleans the extracted data (patient details, diagnoses, dates, and more).
Structured records are stored and made searchable in a central database.