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    AI Automation
    Healthcare
    A build like this typically runs for four months from discovery through production readiness.

    REPRESENTATIVE BUILD

    Reduce Manual Review in Medical Records Processing

    Representative regional health plan scenario with 50,000+ monthly claims and 25 adjusters who review medical records for coverage decisions.

    The Challenge

    Adjusters read medical records that arrive as PDFs, faxes, and scanned images with inconsistent structures.

    Important clinical facts can sit deep inside long records, which makes manual review hard to standardize.

    Peak claim volume creates backlogs, while regulated health data needs strict access and audit controls.

    Our Approach

    A fixed-scope discovery maps lab results, physician notes, imaging reports, decision fields, and exception paths.

    A private pipeline classifies each document and extracts the fields that the claims team defines.

    An evaluation suite tests extraction quality, citations, and failure cases before go-live.

    Adjusters verify each structured extract and correct uncertain fields before the claim workflow uses them.

    The pipeline connects to the claims platform through an API and keeps a full audit trail.

    The client owns the code, prompts, tests, and runbooks. The build has no seat-based price.

    Document Processing Flow

    1

    Document Intake

    The system captures documents from fax, email, and portal uploads.

    2

    Classification

    The system sorts lab results, clinical notes, imaging reports, and other records.

    3

    Data Extraction

    Specialized models extract the clinical fields for each document type.

    4

    Structured Output

    The system formats extracted data for adjuster review.

    5

    Adjuster Review

    Adjusters verify or correct each extract.

    6

    Claims System

    Validated data enters the claims decision workflow.

    What This System Changes

    Adjusters verify a structured extract instead of reading the full record first.
    Source citations and confidence flags direct attention to uncertain fields.
    Exception queues keep coverage decisions under human control.
    Private deployment keeps regulated data inside the approved environment.

    Facing a Similar Challenge?

    We would like to understand your situation and explore how we can help. No sales pressure, just a conversation about what is possible.