Litigation Analytics & Court Data: The Complete Guide
How a team of data scientists turns 325+ million public U.S. court records into clear, useful litigation insight — for researchers, journalists, businesses, and AI agents.
Welcome to the AICasePredict knowledge hub. We are a data-analytics platform — not a law firm — with access to hundreds of millions of public court records. This guide explains, in plain English, how that data becomes useful: how attorney performance is measured, what judicial analytics can and cannot tell you, what lives inside a court record, how predictive models estimate litigation risk, and how to read one of our case reports. Each section links to a deep-dive article, and every article links back here.
A plain-English look at how data scientists turn public court records into attorney win-rate analytics — measuring lawyer performance by judge, court, and case type.
How aggregated court records surface a judge's caseload, case-type mix, and ruling patterns — and why this is statistical context, not a prediction of any single ruling.
How predictive analytics use historical court data to estimate litigation risk — what the numbers can and cannot tell you, and why they are indicative, not legal advice.
A field guide to every field in an AICasePredict case report — parties, attorneys, judge, disposition, and docket entries — plus how AI agents can query the data programmatically.
A practical how-to for searching public U.S. court records — by attorney, judge, court, case type, or date — for researchers, journalists, businesses, and AI agents.
What "public record" really means for U.S. court data — what is open, what is sealed, how access works, and how to request removal — explained by data scientists, not lawyers.
A reference guide to common U.S. court case types — from personal injury and contract to foreclosure and family law — and what each category tells you in the data.