Litera
The unified legal document lifecycle and transaction management ecosystem for the modern law firm.
Predictive litigation intelligence and judicial behavioral data for data-driven legal strategy.
Trellis is a state court research and judicial analytics platform that utilizes advanced Natural Language Processing (NLP) and Machine Learning to structure millions of legal documents into actionable insights. By 2026, the platform has evolved from a simple docket search engine into a sophisticated predictive modeling tool that maps judicial behavior across thousands of jurisdictions. Its technical architecture leverages Large Language Models (LLMs) to extract ruling outcomes from unstructured PDF filings, allowing legal teams to visualize a judge’s grant/deny rates for specific motion types. The system provides a competitive edge by identifying 'judicial tendencies'—statistically significant patterns in how a judge rules on certain legal issues or procedural hurdles. Positioned at the intersection of Big Data and Legal Strategy, Trellis serves as a critical infrastructure layer for Am Law 200 firms and boutique practices alike, offering a searchable database of state court records that were previously siloed or inaccessible. The platform’s 2026 roadmap includes real-time outcome simulation and automated drafting of motion arguments based on a judge’s previous language preferences.
Uses LLM-based extraction to categorize ruling text into 'Granted', 'Denied', or 'Partial' outcomes.
The unified legal document lifecycle and transaction management ecosystem for the modern law firm.
The legal industry's authoritative generative AI platform for research, drafting, and risk management.
Enterprise-grade eDiscovery and AI-driven document intelligence for boutique and mid-market law firms.
Automated legal intelligence and risk scoring for the modern enterprise.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Monte Carlo simulations based on historical ruling patterns and case complexity.
Aggregates performance data on opposing law firms across various case types and judges.
Webhook-based notification system triggered by docket updates or new rulings by specific judges.
Proprietary crawlers for fragmented county-level court websites with optical character recognition (OCR).
Vector-based search across legal filings rather than simple keyword matching.
Generative AI suggestions based on language previously cited as persuasive by a specific judge.
Uncertainty regarding a judge's propensity to grant MSJs in complex tort cases.
Registry Updated:2/7/2026
Adjust the motion's focus to address the judge's historical pain points.
Determining which county court offers the most favorable odds for a plaintiff.
Preparing for a settlement negotiation with an unfamiliar law firm.