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Automating law firms with intelligent AI Copilots and no-code legal workflow orchestration.

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.
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.
Explore all tools that specialize in motion grant/deny rate calculation. This domain focus ensures Trellis Judicial Analytics delivers optimized results for this specific requirement.
Uses LLM-based extraction to categorize ruling text into 'Granted', 'Denied', or 'Partial' outcomes.
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.
Account registration and verification of legal credentials.
Selection of primary jurisdictions (States/Courts) for data tracking.
Integration of internal Case Management Systems (CMS) via API or Clio.
Configuration of Judge Alert notifications for specific active cases.
Training of users on 'Judge Analytics' dashboard interpretation.
Setting up custom motion categories for grant-rate tracking.
Importing active docket lists for automatic monitoring.
Configuring team-wide access controls and research folder hierarchies.
API Key generation for custom data lake integrations.
Final audit of data compliance settings (GDPR/SOC2).
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"Users praise the platform for making state court data searchable like Google, though some find the individual document costs high without a pro plan."
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