Where legal prediction becomes science.
Research, validation, calibration, and model governance for the legal intelligence layer.
Every model documented. Every model traceable.
The registry is the single source of truth for all Splitifi production models. Status, discrimination, calibration date, and training provenance — all documented. No model ships without a complete registry entry.
Vertical-specific research. Universal methodology.
Outcome prediction across custody, support, asset division, and modification proceedings.
Family law corpus, state court dockets, financial disclosure data
Temporal holdout. Calibration review per outcome type.
Every model reviewed as if it were actuarial output.
Time-aware train/test splits. Calibration curves. Leakage remediation. Every model in production has documentation that can survive technical diligence. This is not ML experimentation. This is production data science.
No future data in training. Test sets are strictly chronologically later than training sets.
Calibration error evaluated per model class.
Every input reviewed against the decision time boundary. Post-decision inputs excluded.
Discrimination, calibration date, and training provenance logged per model.
The methodology, documented.
Eight foundational papers covering model governance, calibration standards, glass-box explainability, uncertainty quantification, judicial intelligence, living case state, domain specificity, and network learning architecture.
The four-gate promotion pipeline governing every Splitifi production model. Statistical quality gate, temporal leakage audit, calibration gate, and holdout validation. Promotion is permanent unless performance degradation or calibration drift triggers demotion review.
Calibrated probability requires that predicted frequencies match observed outcomes. Production models carry calibration plots and calibration-error scores in the model registry. A 65% prediction means 65% of comparable cases resolved that way.
Factor attribution for every production prediction: the contributors, their direction and magnitude. The framework is designed to satisfy Daubert and Rule 702 disclosure requirements — documented methodology, known error rate, testable, reproducible.
Confidence intervals on all predictions. A probability is only useful when you know how certain that probability is. Uncertainty quantification separates cases with narrow confidence bands from those with wide ones — different risk profiles requiring different strategy.
Behavioral profiles for verified judges derived entirely from public court records. Ruling patterns, grant rates, procedural tendencies, and judicial consistency scores. No inference — each profile is a statistical summary of recorded decisions across verified case records.
A case is not a static prediction. Each filing, ruling, and negotiation shifts the estimate. Splitifi models maintain a live case state that updates in real time as new events arrive. The settlement zone evolves. The probability updates. Strategy follows the data.
Language models trained on the internet do not know what happens in a specific jurisdiction's courtroom. Domain-specific models trained on labeled legal outcomes outperform general AI on calibrated prediction tasks in law by a statistically significant margin across all tested verticals.
Each case processed deepens every model in the network. The accuracy compound grows every quarter. No competitor starting today can replicate the curve.
Partner with the data lab.
Research partnerships with academic institutions, courts, and legal data science teams. Access to methodology documentation and model governance frameworks.