Join investors, pilot partners, and industry observers tracking our development of privilege-preserving litigation AI with industry-leading hallucination controls.
Graph-based knowledge extraction, Bayesian reasoning, and game-theoretic modeling combine to deliver the reliability, confidentiality, and strategic intelligence litigation demands.
Purpose-built confidentiality-assured infrastructure with no logging, no training, no third-party APIs. Private deployment options.
100% Source Attribution. Graph-based knowledge extraction validates every generated claim links back to original source documents.
Bayesian Reasoning plus game-theoretic modeling generates motion outcome predictions and strategic recommendations.
Transform discovery into structured knowledge
Graph-based extraction builds temporal event sequences from depositions and discovery materials. Every entity, relationship, and timeline receives source attribution—no claims without provenance.
Bayesian reasoning meets game theory
Probabilistic modeling generates motion outcome predictions and identifies critical documents through evidence weighting. Tripartite simulation models client strategy, opponent arguments, and judicial reasoning simultaneously.
Court-ready deliverables with complete audit trails
Produce briefs and strategic memoranda with traceable inference paths from conclusions to original evidence. Meets professional standards other AI systems cannot.
Existing legal AI tools share three architectural limitations that make them unsuitable for high-stakes litigation:
Advised by Princeton Operations Research faculty and AmLaw 100 litigation partners. Built with three years of mathematical rigor and real-world litigation complexity.