Pre/Dicta’s litigation prediction software helps litigators to understand judicial behaviour and predicts how judges will rule. Launched in 2022, the Maryland, United States-headquartered startup acquired Gavelytics at the start of this year. We asked Dan Rabinowitz, CEO and co-founder of Pre/Dicta to tell us more.
How would you describe your company to a friend?
Every case is different, and judges, like anyone else, are influenced by the people involved – attorneys and parties. Does the judge favour attorneys attending a top-ten school or view those as elitists? Are single-party plaintiffs more sympathetic to a judge when a large plaintiff firm represents them? Does that change if it is a product liability or contract case? Traditional legal research does not account for those case-specific factors and their influence on the judge’s decision.
Pre/Dicta’s unique approach analyses the judge’s past ruling history and incorporates judicial demographics data such as education, marital status, career history, geography, financial information, political affiliation, and much more. With these data points, Pre/Dicta can deliver an instant prediction with an 85% accuracy rate.
And if you had to describe it to a techy?
Pre/Dicta uses data science and a docket number to forecast whether a case will survive a motion to dismiss by identifying the factors that influence a judge’s decision-making for the case before them. The most influential variables are the parties, case type, and attorneys. To accurately assess the effect of those variables, the technology accounts for past decisions that have been enriched with data regarding those variables and incorporates biographical information of all federal judges – including net worth, education, work experience, political affiliation, and more – to uncover the hidden patterns in judicial decisions. Pre/Dicta’s ongoing analysis of millions of federal decisions identifies and determines the most relevant case characteristics. It calculates and weighs their impact on a judge’s rulings to predict whether the case will proceed to discovery. Leveraging those insights, Pre/Dicta evaluates how case-specific data points affect a judge. This unique software lets legal teams assess their overall litigation and settlement strategy better.
When were you founded?
Pre/Dicta was launched in June 2022.
Dan Rabinowitz, a former litigator with experience in data science, and Louis Mayberg, a seasoned investor in companies with disruptive technologies, co-founded Pre/Dicta and established one of the vanguard ETF funds that, today, has over $65 million in AUM.
Who are your key managers/senior execs?
Dan Rabinowitz, CEO, and co-founder, is a former litigator at Sidley, the Department of Justice, and in-house. Jonathan Robins, vice president of sales & marketing, has 20 years of experience working with emerging technology startups. Rick Merrill, CEO of Gavelytics, joined forces with Pre/Dicta after the acquisition of Gavelytics.
What is your growth strategy?
At the start of 2023, Pre/Dicta acquired Gavelytics, which amassed large datasets of state court data. The acquisition permits Pre/Dicta to expand its predictive offering to encompass state courts.
Have you received investment?
No, we are self-funded.
Who are your target clients?
Pre/Dicta provides objective, measurable, and accurate predictions to verticals that require certainty. Those include litigators, plaintiff and defense attorneys, in-house counsel, insurance companies, and litigation financiers.
Have there been any fundamental changes in direction since you were founded?
Since Pre/Dicta is the only litigation analytics platform that makes predictions on federal cases, acquiring Gavelytics will allow the platform to expand into state court cases. This will enable litigators to generate predictions for every lawsuit nationwide.
What are the key challenges you face in your market?
The entrenched mindset of users regarding the capabilities of legal analytics. We are aware of the disclaimer that attorneys are obligated to provide their clients “past performance is not indicative of future results.” For years, companies providing backward-looking statistics dominated the litigation analytics space. Users relied upon those, despite having a limited value in providing accurate and reliable predictions. Exposing users to our advanced capabilities and the algorithmic models and AI is unfamiliar territory for potential users and, at times, requires background beyond our capabilities.
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