Learned Hand is an AI‑driven platform that automates fact extraction, issue mapping, and structured argument generation for trial judges, clerks, and staff attorneys. It links every citation to its source for one‑click verification and produces output that conforms to local court rules and motion‑specific procedures, helping legal staff work faster and more accurately on high‑volume casework.
Funding
Funding not disclosed
Founders
Product
Problem
Trial judges increasingly face high caseloads and a growing volume of motions while often lacking dedicated clerks or sufficient staffing, leading to time‑consuming manual research and verification. This resource gap hampers efficient case preparation and increases the risk of errors in citation and argument development.
Solution
Learned Hand provides an AI‑driven platform that automates the extraction of factual information, maps legal issues, and structures arguments for judicial decision‑making. The system links every citation directly to its source in the record, enabling one‑click verification and reducing the time spent on manual cross‑checking. By adhering to local court rules and motion‑specific procedures, the output aligns with the precise formatting and procedural requirements of each jurisdiction. The tool enhances the productivity of clerks and staff attorneys, allowing them to work faster and deeper while giving judges without dedicated support a reliable analytical backup.
Target Audience
Primary users are trial judges, judicial clerks, and staff attorneys who need efficient, accurate legal analysis and citation verification for high‑volume casework.
Features
- Automated fact extraction and issue mapping from case records
- Structured argument generation with citation linking to original sources
- One‑click verification of citations and claims to eliminate manual cross‑checking
- Customizable output that conforms to local court rules and motion‑specific procedures
- Dashboard that presents organized analysis for judges, clerks, and staff attorneys