Analyzes litigation data to predict outcomes, profile judges and opposing counsel, identify winning strategies, and surface risks—transforming gut instincts into data-driven litigation decisions.
"We had a patent case last year—$35 million exposure. Went through 18 months of litigation, $3 million in fees, two rounds of summary judgment briefing. Lost. Here's the thing: that judge had granted summary judgment to defendants in 78% of similar cases. The opposing counsel had lost 4 of their last 5 cases before her. Our case was weak on claim construction—and she's strict on claim construction. All of this was knowable. All of this was in the public record. But we didn't know it because we don't systematically analyze litigation data. We relied on gut feel and war stories. We told the client they had a 'reasonable' chance. Turns out reasonable meant 22%. If we'd known that upfront, we would have settled for $4 million instead of spending $3 million to lose $35 million. That's the cost of flying blind."
— Litigation Partner, AmLaw 100 Firm
Deploy an AI that analyzes millions of court records, judge rulings, counsel track records, and case outcomes to give you the intelligence you need to make informed decisions—not educated guesses.
Probability-weighted outcome analysis based on case type, jurisdiction, judge history, and comparable cases. Know your odds before you spend your budget.
Deep profiles on every judge and opposing counsel. Ruling patterns, win rates, procedural tendencies, argument preferences. Know who you're facing.
Data-backed strategy suggestions based on what's worked in similar cases, with this judge, against this counsel. Turn historical patterns into tactical advantages.
Complete profiles on judicial behavior, ruling patterns, and case management style.
Intelligence on counsel track record, litigation style, and strategic tendencies.
Probability analysis of case outcomes based on comparable matters and key factors.
Find and analyze similar cases to benchmark strategy and outcomes.
Track motion success rates and optimize filing strategy.
Analyze damages awards and settlement values by case type and venue.
New patent infringement case with $35M damages claim. Traditional assessment would take months. Agent analyzed judge history, opposing counsel record, and comparable cases in hours.
"Case assessment: TechCorp v. Client (Patent Infringement). Judge: Hon. Richard Martinez, E.D. Texas. Judge profile: 89 patent cases in 10 years. Plaintiff win rate: 72%. Summary judgment grant rate: 34% (defendant), 58% (plaintiff). Damages: Awards above requested amount in 23% of cases. Known for strict claim construction, favors technical experts. Opposing counsel: Smith & Partners. Patent win rate: 67% (41 cases). Settlement rate: 45% pre-Markman, 78% post-Markman. Lead partner lost 2 of last 3 trials. Comparable cases: 12 cases with similar patent type and damages claim. Median settlement: $8.2M (24% of claimed damages). Defendant verdict: 4 cases (avg damages: $0). Plaintiff verdict: 3 cases (avg damages: $28.4M). Recommendation: Case presents high risk. Judge's pro-plaintiff tendencies combined with strong opposing counsel suggests 28% defendant success probability. Expected value analysis: Fight ($35M × 72% = $25.2M exposure) vs. settle ($8-12M range). Recommended action: Engage in early settlement discussions targeting $4-6M range before Markman hearing. Morrison historically settles for 30% of claimed damages when defendant shows willingness to litigate."
Summary judgment motion in employment discrimination case. Agent analyzed judge's ruling patterns and successful argument structures to optimize motion strategy.
"Judge profile: Hon. Sarah Chen, N.D. California. Employment discrimination cases: 67 in last 5 years. MSJ defendant grant rate: 42% overall. However, pattern analysis reveals: When defendant leads with procedural exhaustion arguments: 38% grant rate. When defendant leads with legitimate business reason + documentation: 71% grant rate. When defendant includes statistical analysis of comparable employees: 78% grant rate. Judge Chen has cited 'lack of comparator analysis' as basis for denial in 23 of 39 denied motions. Writing style preference: Judge Chen's own opinions average 4,200 words. Successful motions before her average 28 pages with extensive fact section. She has criticized 'conclusory' arguments in 34% of denied motions. Opposing counsel analysis: Plaintiff's counsel (Williams Law) has opposed 12 MSJ motions in employment cases. Their opposition success rate: 67%. However, they have lost 4 of 5 motions where defendant provided robust statistical comparator analysis. Recommended strategy: (1) Lead with legitimate business reason supported by contemporaneous documentation. (2) Include statistical analysis of 5+ comparator employees. (3) Detailed fact section—minimum 12 pages. (4) Avoid procedural arguments in first 10 pages. (5) Cite Johnson v. TechCorp (N.D. Cal. 2023)—Judge Chen's own precedent on comparator analysis."
Breach of contract dispute with $15M claim. Opposing counsel demanding $8M settlement. Agent analyzed their settlement patterns and provided negotiation intelligence.
"Settlement analysis: GlobalContract v. Client. Opposing counsel: Jennifer Davis (Davis & Partners). Settlement behavior analysis (47 matters): Initial demand pattern: Opens at 60-75% of claimed damages. Final settlement pattern: Averages 22% of claimed damages when defendant demonstrates willingness to litigate. Timeline pattern: Makes significant concessions after discovery deadline (78% of cases). Makes largest concessions within 30 days of trial (91% of cases). Bluff indicator: High. Has proceeded to trial in only 3 of 47 matters (6%). Of 3 trials, lost 2. Walk-away analysis: Davis has walked away from settlement talks in 2 of 47 cases. Both involved matters under $500K—doesn't litigate small cases to verdict. Comparable settlements: 8 breach of contract cases with similar damages in this jurisdiction. Range: $1.8M - $4.2M. Median: $3.1M. Recommendation: Current $8M demand is 2.5x market rate. Counter at $2M (13% of claim). Davis will counter. Hold firm through discovery deadline—expect 40% reduction. Final settlement range: $2.8-3.4M (19-23% of claim). Do not accept above $4M—above 75th percentile for comparable cases."
Complex damages case requiring economic expert. Agent analyzed judge's expert exclusion patterns and opposing counsel's Daubert track record to optimize expert selection.
"Expert witness strategy: Johnson v. MegaCorp (Antitrust). Judge's expert rulings: Hon. Michael Torres has ruled on 34 Daubert motions in antitrust cases. Exclusion rate: 41% overall. However, pattern analysis: Regression analysis experts: 28% exclusion rate. Survey experts: 67% exclusion rate. Judge has explicitly stated preference for 'rigorous quantitative methodology' in 12 opinions. Opposing counsel Daubert history: Morrison & Partners files Daubert motions in 89% of cases. Success rate: 52% overall. However, against regression-based damages experts: 23% success rate. Their challenges typically focus on: (1) data reliability, (2) alternative explanations not considered. Expert recommendation: Primary: Dr. Sarah Williams (Stanford Economics). Daubert survival rate: 94% (17 of 18 challenges). Specific to this judge: Testified twice, both times survived challenge. Methodology: Regression-based with robust sensitivity analysis. Known weakness: Opposing counsel may challenge her 2019 paper—prepare rebuttal. Secondary: Dr. James Chen (Berkeley). Daubert survival: 88%. Has not appeared before Judge Torres. Strong on data reliability. Avoid: Dr. Michael Roberts (frequent expert). Excluded by Judge Torres in 2021. Opposing counsel will cite Torres's own criticism of his methodology."
Probability-weighted analysis of case outcomes based on historical data and key factors.
Complete judicial analytics including ruling patterns, grant rates, and preferences.
Opposing counsel track records, settlement patterns, and strategic tendencies.
Comparable settlement data and negotiation intelligence for optimal outcomes.
Success rate analysis by motion type, judge, and argument structure.
Expert witness track records, Daubert history, and judge-specific success rates.
Verdict and damages analysis by case type, venue, and comparable matters.
Find and analyze similar cases to benchmark strategy and outcomes.
Duration predictions and deadline optimization based on historical patterns.
Reports to: Litigation Partner / GC
Availability: 24/7
Scope: All litigation matters
Complete specification including prediction models, data sources, and analysis templates.
Download .docxConfigure data sources, customize prediction models, and define practice-specific analysis parameters.
Pay once. Own the asset. Full source code. Deploy across all matters.
All predictions, strategies, and intelligence never leave your infrastructure.
New prediction models, data integrations, and analysis improvements.
Configure data sources, models, and practice-specific analysis parameters.
Deploy the Litigation Analyzer Agent on your infrastructure. Data-driven decisions. From day one.
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