Not generative AI.
Calibrated outcomes.
Law has operated on judgment and precedent for two centuries. Splitifi produces the first calibrated probability infrastructure — not inference, not text generation, not legal search.
"We don't summarize law. We predict it."
Where Splitifi sits in the landscape.
From court record to calibrated probability.
Real court records, deduplicated, normalized to a common schema. Every resolved outcome, every jurisdiction, real data only. Zero synthetic rows in production.
One model per jurisdiction and practice-area combination. Time-aware train/test splits enforce strict temporal separation — no future data leakage by construction.
Minimum discrimination gate. calibration-error verification. 12-month holdout validation on unseen data. Failed models are logged, published, and excluded from production.
factor attribution on every prediction. Glass-box explainability — which factors drove the outcome, direction, and magnitude. No black-box inference.
What it takes to ship a model.
Who the methodology serves.
A probability you can defend.
A calibrated outcome score with factor attribution you can interrogate, explain to a client, and defend under cross-examination. Not a summary. Not a recommendation. A number with a documented error rate.
IRR modeling on real signal.
IRR and MOIC modeling built on calibrated win probability, settlement range, and duration estimates — not analyst heuristics. Production models across 22 litigation finance categories.
The deterministic layer that makes your LLM defensible.
Language models infer. Splitifi measures. The deterministic prediction layer that grounds your AI product in calibrated probability when a general counsel asks how you got the number.
The methodology survives technical diligence.
discrimination scores, calibration-error reports, holdout curves, attribution logs, and the full model registry are available under NDA for qualified data room review.