"The audit is not a demo. It is a diagnostic run on your own portfolio data. The output is not a capability statement — it is a finding. Something you did not know about your own book before the audit ran."
A specific finding about your own book.
Not a capability statement. A list of cases you should act on, and why.
Run the Criterica model fleet against your existing portfolio. See win-rate forecasts, duration distributions, settlement probability curves, and concentration risk by case type, venue, and attorney — using your own book.
Measure reserve adequacy against reliable outcome models. Identify claims with high litigation propensity before they file. Score settlement timing against jurisdiction-specific duration models. Find the variance between your current reserves and what the data says.
Benchmark your case portfolio against outcomes from 475M+ comparable matters. Score case quality, flag underperforming counsel, and identify venue-specific risk before the case reaches trial. Outside counsel performance — measured, not estimated.
Stress-test your capital structure against the underlying legal asset risk. Model recovery timelines, identify concentration exposure by circuit and case type, and quantify the gap between your current capital deployment and model-derived risk.
Assess the quality, completeness, and predictive value of your legal data before you build on it. Identify missing features, labeling gaps, and jurisdiction coverage holes. The audit your data needs before your models can trust it.
Measure outside counsel performance against a jurisdiction-matched benchmark of comparable matters. Control for case type, venue, and opposing counsel. Identify the performance gap your panel management process cannot see.
Model docket velocity, case flow patterns, and delay concentrations across court divisions. Identify systemic inefficiencies using multi-year case timing data. Intelligence for court administrators who need to manage resources against actual caseload patterns.
Score your medical-legal exposure using healthcare liability models trained on real court records. Identify high-risk claim types, venue concentration, and provider-level exposure. Reserve against what the models see, not what the billing system shows.
Measure your employment litigation exposure against jurisdiction-specific models for discrimination, wrongful termination, wage and hour, and FMLA claims. Identify the cases most likely to become high-cost matters before they escalate.
Score active and potential construction disputes against outcome models trained on federal and state court data. Identify contract risk, venue exposure, and arbitration propensity by project type, counterparty, and claim size.