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About

AI for Legal is the most complete searchable database of real AI implementations in legal. Built for general counsel, legal operations leaders, and law firm partners evaluating AI adoption.

What is AI for Legal?

AI for Legal is the largest open database of real AI implementations in legal. We catalog what law firms, corporate legal departments, and legal technology companies have actually done with AI — the use case, the technology, and the measurable results — so that general counsel, legal operations leaders, and law firm partners can make informed decisions based on evidence, not vendor marketing.

Our Methodology

Every case study in our database goes through a structured collection and verification process. We do not fabricate data, generate synthetic results, or accept unverified claims.

Data Sources

Case studies are collected from three categories of sources:

  • Vendor-published case studies — documented implementations from legal AI providers such as Harvey AI, Luminance, Relativity, Ironclad, and others.
  • Independent research — reports from industry publications like Artificial Lawyer, Legaltech News, and consulting firms that document specific deployments.
  • Community contributions — case studies submitted directly by vendors and legal departments, verified by our editorial team before publication.

Quality Levels

Each case study is assigned one of three quality levels:

  • Verified — complete content with at least two quantified metrics, full taxonomy classification (practice area, use case, AI technology), and a traceable source.
  • Contributed — submitted by a vendor or legal department, reviewed by our team, and published with attribution.
  • Scraped — programmatically collected from public sources. Contains structured data but may have shorter content sections.

Taxonomy & Classification

Every case study is classified across four dimensions: practice area (8 categories), use case type (10 categories), AI technology (10 categories), and company size. This standardized taxonomy enables cross-comparison across implementations and helps surface patterns — for example, which AI technologies deliver the strongest ROI for specific practice areas.

Editorial Standards

  • Metrics are reported exactly as published by the source — we do not round, extrapolate, or reinterpret results.
  • Every case study links back to its original source when available.
  • We distinguish between vendor-reported results and independently verified data.
  • Case studies without quantifiable results are still included if they document a real implementation with a named organization.

About Us

We are a small team focused on making AI adoption in legal more transparent and evidence-based. Our background spans legal operations, data engineering, and legal technology deployment.

Have questions, corrections, or a case study to share? Feel free to reach out.