Free Guide for Attorneys, Paralegals & Legal-Ops Teams
A practical guide to extracting and organizing contract data with AI.
How attorneys, paralegals, and legal-ops teams replace manual legal document data entry with an upload-review-export workflow.
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First-pass contract review is high-cost, low-leverage work.
NDAs, vendor agreements, and employment contracts all need the same repetitive extraction.
Key clauses — auto-renewals, indemnification, governing law — get missed under deadline pressure.
Paralegals spend hours on data entry that should take minutes.
Managing hundreds of agreements with no structured data is an operational risk.
Four steps, from document to structured data:
Contracts, NDAs, employment agreements, and other standard legal documents as PDFs or scans.
Parties, effective and expiration dates, payment terms, obligations, governing law, and termination/renewal clauses.
Confidence scores route attorney attention to uncertain extractions — the tool organizes; your team judges.
Export structured terms to CSV/Excel or push them through the API to make your agreements searchable.
Combine automation with explicit review and source-document checks. Test representative inputs, use confidence signals, and define quality checks before you automate higher-volume work.
Convert supported legal documents into consistently named data for review and export.
Route uncertain fields to a person instead of treating every extracted value as equally reliable.
Evaluate the workflow with your own layouts and image-quality range before scaling.
Move from manual uploads to an integrated workflow after review rules are established.
Verify sensitive tax, legal, medical, and financial fields before downstream use.
Every extraction returns these fields as structured data with per-field confidence scores:
Extracted contract data exports as CSV/Excel or JSON, feeding contract trackers, obligation calendars, and document-management systems. It organizes documents for review — it is not legal advice and does not replace counsel.
The AI Document Automation Playbook
A free PDF playbook covering AI extraction workflows, quality control, batch processing, and API integration. Its examples come from tax-document operations, and the workflow chapters apply directly to legal document processing.
9 practical chapters covering evaluation, review, integration, and operations.
Identify where legal document data enters, who reviews it, and where the approved result goes.
Build a test set covering common layouts, scans, photos, and known edge cases.
Upload a supported file and inspect the returned fields, structure, and confidence signals.
Set thresholds and source-document checks for fields that matter to the decision or filing.
Map reviewed JSON, CSV, or Excel fields to the destination system.
Introduce higher-volume processing only after the single-file workflow is reliable for your inputs.
Use authentication, retries, validation, and exception handling in an integrated workflow.
Confirm access, retention, deletion, and audit requirements for the data you process.
Track review outcomes, add edge cases to the test set, and revisit thresholds as layouts change.
Intake forms, processing checklists, and quality-control templates.
A consistent checklist for comparing document-parsing tools and workflow fit.
Authentication, structured output, validation, and error-handling patterns.
Document type; Parties (names, roles); Effective & expiration dates; Payment terms; Key obligations; Governing law; Termination & renewal clauses. Each field comes back with a confidence score, and results export to JSON, CSV, or Excel.
No. The core workflow is upload, review, and export, all in the browser. Developers can additionally use the REST API and batch upload, but nothing requires code.
PDF (including scanned), PNG, JPG, and WEBP. Native PDFs, scans, and phone photos all work, with confidence scores flagging anything hard to read.
Your first 3 documents are free with no credit card. Paid plans start at $9/month, and every plan includes API access and all export formats.
No. It focuses on document intake, representative testing, confidence-based review, structured exports, API integration, and exception handling.
The early chapters use the browser-based workflow. The API section is optional and intended for teams that want a programmatic integration.
The core workflow is tool-agnostic and emphasizes evaluation criteria you can apply to document-extraction products, including this one.
Check this page for the current version as the product and recommended review practices evolve.