Predictive Litigation Intelligence: Winning Before You File

The Guessing Game

Litigation has always been part science, part art, and part gambling. Even the most experienced trial attorneys make critical strategic decisions—whether to file in state or federal court, whether to pursue summary judgment, whether to settle or go to trial—based primarily on intuition and anecdotal experience.

The data to make these decisions scientifically has always existed. Every court docket, every judicial opinion, every jury verdict, every settlement amount is a data point. But the volume is overwhelming. There are over 400,000 federal civil cases filed annually, plus millions of state court cases. No human can process this volume to identify actionable patterns.

The Information Asymmetry

This creates a massive information asymmetry. Large firms with decades of experience before a particular judge have an intuitive sense of that judge's tendencies. Solo practitioners and smaller firms operate essentially blind, making strategic decisions without the benefit of pattern data.

Vigil's Litigation Intelligence Engine

BasaltVigil's Predictive Litigation Intelligence module eliminates this asymmetry by converting the entire universe of litigation data into actionable strategic intelligence.

1. Judge Analytics

Vigil maintains comprehensive analytical profiles for every active federal judge and state court judges in major jurisdictions. These profiles include:

• Grant rates for motions to dismiss, summary judgment, and Daubert challenges
• Sentencing patterns and penalty ranges for specific violation types
• Procedural preferences including discovery dispute resolution tendencies
• Time-to-disposition averages and scheduling patterns
• Appellate reversal rates broken down by issue type

When you're deciding whether to file in the Southern District of New York versus the District of Delaware, Vigil provides a quantified comparison of your expected outcomes before each judge in both jurisdictions, based on the specific claims and defenses in your case.

2. Win Probability Modeling

Before filing or responding to a complaint, Vigil generates a Win Probability Assessment based on:

• Historical outcomes for similar cases (matched by claim type, industry, jurisdiction, and dollar amount)
• The specific judge's track record on the relevant legal issues
• The strength of your factual allegations compared to cases that survived motions to dismiss
• The opposing counsel's historical win rate and strategic tendencies

This is not a magic eight ball. It is a rigorous statistical model trained on millions of case outcomes, providing confidence intervals and sensitivity analyses that help you make informed strategic decisions.

3. AI-Powered Deposition Preparation

Vigil's deposition preparation module analyzes the witness's prior testimony across all available depositions, trial transcripts, and regulatory proceedings. It identifies:

• Statements that contradict the witness's current position
• Topics where the witness has historically been evasive or imprecise
• Technical areas where targeted questioning is most likely to elicit favorable admissions
• Optimal question sequencing based on psychological models of witness behavior

The system generates a complete deposition outline with specific questions, predicted responses, and follow-up branches for different answer scenarios.

The Settlement Calculator

Perhaps the most valuable output of the Litigation Intelligence Engine is its Settlement Valuation Model. By analyzing outcomes in comparable cases, the system generates a settlement range with probability-weighted expected values. This converts the settlement negotiation from an art of persuasion into a data-driven economic analysis.

When you know that cases with your fact pattern settle for between $2.1M and $4.7M with a median of $3.2M, and that going to trial has a 62% win probability with an expected verdict of $5.8M but a 38% probability of a defense verdict, the optimal settlement strategy becomes a straightforward calculation rather than a guess.