Pre/Dicta, a legal analytics company based in New York, announced a significant expansion of its predictive modeling platform. The enhancement includes appellate forecasting capabilities, biographical intelligence tools, and comparative prediction features that now extend across judges, venues, and law firms. This development aims to help legal professionals navigate the complexities of judicial decision-making with greater foresight and precision.
The company is known for its 85% accuracy rate in predicting outcomes of motions to dismiss and is now broadening its offerings to cover the entire litigation lifecycle, from pre-suit analysis to appellate proceedings.
The most notable enhancement is the appellate forecasting feature, which enables users to assess both the likelihood of an appeal and the probability of reversal in federal cases. By analyzing behavioral patterns, rather than traditional legal precedents, the platform identifies historically similar cases, known as “doppelganger” cases, through its predictive analytics. This capability covers both federal and California state cases.
In addition to appellate forecasting, Pre/Dicta has broadened its motion coverage to include various other legal motions, such as temporary restraining orders and Daubert challenges. The update brings the total number of motion modules to ten.
The platform’s biographical intelligence tools allow users to isolate judicial characteristics, such as political affiliation and educational background, to understand their impact on case outcomes. This feature is particularly useful for analyzing decision-making patterns based on judges’ backgrounds.
The new comparative prediction feature lets users simulate different case scenarios by altering variables like law firms or venues while keeping other elements constant. This is particularly beneficial for tasks such as counsel selection and venue shopping.
Pre/Dicta introduces “Precedent Intelligence Cases,” a feature allowing users to explore historical cases that underpin the platform’s predictions. These cases can be filtered by outcome, letting users focus on successful precedents.
Unlike traditional legal research tools that focus on precedent or basic win/loss rates, Pre/Dicta employs machine learning on a database of 15 million federal litigation cases, with extensive variables associated with each case. This approach allows the platform to generate probabilities based on complex correlations between numerous data points.
The expanded platform, which includes a redesigned user interface, appeals to a wide range of legal professionals. Insurance companies use it for early claim assessments, while law firms employ it for motion preparation and strategic planning. Additionally, mediators utilize the platform during settlement negotiations to offer objective data-driven probabilities.