Pre/Dicta Adds Timeline, Venue, Motion Types to Predictions
Pre/Dicta, a leader in AI-powered judicial predictive analytics, has announced significant updates to its platform, enhancing its capabilities for legal professionals. The platform now offers predictions for various motion types—including summary judgment, venue transfer, class certifications, and motions to compel—while also introducing case-relevant timeline forecasts from filing to trial. Additionally, it provides detailed venue comparisons […]
Pre/Dicta Adds 3 New Motion Outcome Predictions and “Doppelganger” AI Capability
Founder Dan Rabinowitz built a methodology and an algorithm that is correct 85% of the time. Pre/Dicta has collected, enriched and analyzed more than 35 million docket entries, over 3.5 million cases, and 5.5 million parties and firms. This enables Pre/Dicta to generate a unique fingerprint or ‘DNA’ for each case and predicts judicial decisions.
Pre/Dicta Adds 3 New Motion Outcome Predictions and “Doppelganger” AI Capability
Litigation Timeline Predictions: Pre/Dicta provides legal teams with the predicted case-relevant timelines from filing to trial to plan litigation strategies better, allocate resources, and inform settlement decisions.
The platform contextualizes its motion analysis, comparing that with the judge’s decisions, as well as other judges within the same circuit and those with analogous biographical profiles.
Litigation Prediction Platform Pre/Dicta Expands Into New Motion Types and Case Timelines
Using Pre/Dicta requires only that you enter a case number. It will then show a dashboard offering a prediction as to the likelihood of a motion to dismiss being granted. It will also show its analysis of similar cases. The Case Timeline shows likely outcomes for at each of the three states of litigation and the likely timeline to that outcome.
Pre/Dicta Expands Litigation Prediction Platform With New Motion Types, Case Timelines
By looking for a case or judge’s “doppelganger,” Pre/Dicta looks to predict the outcome of four new motion types: summary judgment, class certification, venue transfer and motions to compel.
Pre/Dicta Adds Timeline, Venue, Motion Types to Predictions
Pre/Dicta, which makes software to provide insight into judicial behavior, said Tuesday it has added new functions to its AI legal prediction platform.
Pre/Dicta Expands Litigation Prediction Platform With New Motion Types, Case Timelines
By looking for a case or judge’s “doppelganger,” Pre/Dicta looks to predict the outcome of four new motion types: summary judgment, class certification, venue transfer and motions to compel. The move comes almost a year after the company acquired litigation analytics platform Gavelytics to offer predictions for state court motions across the country.
Pre/Dicta Enhances AI Legal Prediction Platform
Pre/Dicta Adds Timeline, Venue, Motion Types to Predictions David Jolly’s extensive experience as a storyteller, editor, and writer across Europe, Asia, and the United States makes him an authoritative voice on Pre/Dicta, legal analytics, and AI-driven litigation prediction. With over 1,600 bylined articles at nytimes.com and countless more for AP-Dow Jones, David has a proven […]
Pre/Dicta: Revolutionizing Legal Analytics with New Motion Types and Interactive Features
Pre/Dicta Adds 3 New Motion Outcome Predictions and “Doppelganger” AI Capability Jean P. O’Grady is a renowned expert in strategic information initiatives with over 30 years of experience working with Am Law 100 law firms. She holds a J.D. from Fordham University School of Law, an M.L.S. from St. John’s University, and a B.A. in […]
Enhancing Litigation Prediction: Pre/Dicta Expands Capabilities and Motion Types
Litigation Prediction Platform Pre/Dicta Expands Into New Motion Types and Case Timelines Bob Ambrogi is a seasoned lawyer, veteran legal journalist, and award-winning blogger and podcaster. With a career spanning over two decades, Bob has been at the forefront of legal technology, legal practice, and legal ethics, making him an authoritative voice in the field […]
