Can it be relied on for review?

As a first pass, not as the review. It finds and flags; a fee earner decides. The value is that attention goes to the three unusual clauses rather than to confirming that forty standard ones are standard.

How does it handle scanned or poorly formatted contracts?

OCR and layout parsing run ahead of extraction, and confidence scoring routes poor-quality documents to manual review rather than posting an uncertain extraction as fact.

What accuracy should we expect?

Clean native PDFs of a consistent format extract very reliably. Poor scans and highly variable layouts are harder. We benchmark on your actual documents before committing to a threshold, and the confidence scoring means low-certainty extractions get reviewed rather than silently posted.

Do we still need someone reviewing documents?

For a much smaller share. The goal is that your team reviews the exceptions rather than every document — which typically means a fraction of the volume, handled in seconds each rather than minutes.

Document AI · Law Firms

Document AI for Law Firms

First-pass contract review is expensive and profoundly repetitive.

Reviewing a contract for standard provisions is necessary, high-volume, and a poor use of legal time. Document AI extracts the clauses, dates, parties, and obligations, flags deviations from your standard positions, and gives the fee earner a marked-up starting point instead of a blank one.

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Per document: SecondsPer document
Field confidence: ScoredField confidence
Full trail: AuditedFull trail

Typical stack

Azure Document AI
OpenAI Vision
Python
Postgres
Queue Workers

Use cases

What this handles for law firms

Extract parties, dates, values, governing law, and termination provisions automatically
Flag clause deviations from your firm's standard positions and playbook
Build obligation and key-date registers across a portfolio of agreements
Process bulk due diligence document sets in hours instead of weeks

First-pass review completed before a fee earner opens the document, with exceptions already highlighted.

Context

The law firms problem underneath it

Legal work is document work, which makes it unusually well suited to AI — provided every output is grounded in a real source. We build systems that retrieve from your actual matter files, statutes, and precedents and cite what they used, so the work product can be checked rather than trusted.

Junior time disappears into research and first-pass document review
Precedent and know-how are scattered across matters nobody can search effectively
Client intake and conflict checking are manual and slow the engagement
Contract review is repetitive but too consequential to rush

What we build

Inside a document AI pipeline

We design for the ninety percent and route the rest to people. Documents that extract cleanly and pass validation flow straight through. Anything below your confidence threshold, or failing a business rule, lands in a review queue where a person fixes it in seconds — and that correction feeds back into extraction quality.

OCR and layout parsing for scans, photos, native PDFs, and email attachments
Field extraction with per-field confidence scoring rather than all-or-nothing output
Validation rules that check totals, dates, references, and cross-document consistency
Human review queues for low-confidence extractions with single-screen correction
Straight-through posting into ERP, accounting, or CRM systems
Classification that routes each document type to the right pipeline automatically

Deliverables

What ships with the engagement

Extraction schema per document type agreed with your team
Accuracy benchmark measured on a sample of your real documents
Review queue interface for exceptions
Integration writing extracted data into your system of record
Retention and audit configuration matching your compliance requirements

FAQ