Shehebar Law
Internal legal operations tool for a New York/New Jersey attorney handling SRO proceedings and complex litigation — replacing hours of manual document review with AI-driven classification and extraction.
ZipLyne built an end-to-end document pipeline: Claude Sonnet classifies exhibits, Mistral handles OCR on scanned files, pdf-lib stamps Bates numbers, and a separate Claude-powered module surfaces analogous SRO decisions — all from a single upload interface.
From upload to production-ready exhibit package.
Upload any PDF, image, or scan. Claude reads the document, classifies the exhibit type (financial record, communication, corporate filing, etc.), and returns a label with a confidence score.
Mistral handles OCR on scanned and image-based documents, then extracts case references, party names, dates, and dollar amounts into structured JSON — ready for downstream processing or export.
Configurable prefix (case number + client code) applied via pdf-lib. Stamped PDFs generated and packaged into an organized bundle ready for production — no manual numbering, no errors.
Attorney inputs key matter facts. Claude searches and summarizes analogous Special Review Officer decisions, returning relevant rulings with excerpts — hours of case research in under a minute.
Two models, purpose-assigned.
Reads full documents to determine exhibit type and confidence score. Also powers the SRO research module — surfacing analogous rulings based on matter facts.
Handles OCR on scanned and image-based documents. Extracts structured entities — party names, case references, dates, dollar amounts — into JSON for downstream use.
Files stream through the pipeline — no storing full PDFs in the database. Multer handles uploads, pdf-lib handles stamping, output bundled for download on completion.
What it runs on.
Manual document work
is the first thing to automate.
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