Legal Knowledge Extraction
Legal knowledge extraction is the automated conversion of unstructured legal documents—such as contracts, regulations, policies, and case law—into structured, machine-readable data. Instead of lawyers and analysts manually reading, annotating, and tagging thousands of pages, systems identify entities (parties, dates, monetary amounts), clauses, obligations, exceptions, references, and relationships between them. The result is a legal knowledge graph or structured database that can be queried, searched, analyzed, and reused across matters. This application matters because legal work is heavily text-centric and traditionally very manual, driving high costs, slow turnaround times, and inconsistency in analysis. By using AI to systematically extract and normalize legal concepts at scale, firms and in-house legal teams can enable powerful downstream capabilities: faster document review, better compliance monitoring, richer legal analytics, and smarter drafting assistance. It becomes the foundational layer that turns a firm’s document archive into an operational knowledge asset rather than static files.
The Problem
“Turn unstructured legal docs into queryable entities, clauses, and relationships”
Organizations face these key challenges:
Clause and entity extraction is inconsistent across reviewers and law firms
Due diligence and regulatory mapping take weeks due to manual reading and tagging
Hard to answer questions like “where do we have change-of-control risk?” without re-review