About

What is AI for Legal?

AI for Legal is a searchable directory of documented AI implementations in legal. Each record captures the use case, technology when classified, outcomes reported by the source, and the source link. General counsel, legal operations leaders, and law firm partners can review the details behind an implementation before evaluating a vendor.

How the directory works

Entries come from public sources and direct submissions. Automated checks look for required fields and source availability before publication. They are not a human fact-check or an independent audit. We do not generate synthetic results or fill missing fields with assumptions.

Sources

Each entry retains the source name and link recorded during collection. Sources may include vendor case studies, industry publications, public filings, conference talks, and direct submissions. The directory does not assign a source type unless that provenance is explicitly recorded.

• Source identity — the recorded source name and a direct link to the cited page.
• Source timing — a publication date only when it exists in the record, plus a separately labelled source-link check date.
• Submission attribution — contributed records identify the submitting organization when that information is available.

Record labels

Each case study is assigned one of three quality levels:

• Verified — reserved for records whose status explicitly records a human review. A complete record and reachable source alone do not earn this label.
• Contributed — submitted by a vendor or legal organization and published with attribution; any additional review state is recorded separately.
• Scraped — programmatically collected from public sources. It may have shorter content sections.

Classification

Records use four comparison dimensions where the data supports them: practice area (7 categories), use case type (10 categories), AI technology (8 categories), and company size. Missing values remain marked as unavailable rather than inferred. The taxonomy lets readers compare documented use cases and technologies without filling gaps in the source.

Editorial Standards

• Metrics are reported exactly as published by the source — we do not round, extrapolate, or reinterpret results.
• Every published case study links to the cited source recorded for that entry.
• Source-reported outcomes stay attributed to the source and are not presented as an independent audit.
• Records without quantifiable results can still be included when they document an implementation with a named organization and meet the directory's evidence requirements.

About Us

We maintain this directory for people evaluating AI in legal. Our background spans legal operations, data engineering, and legal technology deployment.

Questions, corrections, or a case study to share? Contact us.