Real data only.
No synthetic augmentation. No proxies. No imputed labels. Every model in the Criterica fleet is trained on real adjudication data drawn from our proprietary outcomes corpus of real court records spanning federal courts, state courts, and international jurisdictions. Synthetic data in legal AI compounds errors at the output layer because training distributions do not match real adjudication patterns. The only way to build a model that performs under the pressure of institutional capital is to train it on the actual thing.
Jurisdiction-specific models.
Not generalist classifiers. A model trained on SDNY cannot generalize to the 9th Circuit without systematic error. Jurisdictions differ structurally — in local rules, jury pool demographics, judge temperament, procedural norms, and historical base rates. These are not noise. They are signal. The Criterica fleet is built one model per jurisdiction-case-type combination, not one model for everything.
Probabilities, not labels.
The output is a number. 0.73 means a 73 percent chance, and it is built to mean that, right about 73 times out of 100. Not "high confidence." Not a sentiment label. Not a traffic light. Litigation funders, insurers, and enterprise legal teams allocate capital against probabilities, not categories. Labels destroy the information that makes allocation possible. Every Criterica model outputs a real probability you can compare directly across cases, jurisdictions, and time.
We hold back models that cannot prove themselves.
A model stays on the shelf until it can prove itself against real outcomes it was never shown. A model that looks perfect is treated as a warning sign, not a win, because it usually means it had already seen the answer. A meaningful share of the models in the registry are held back as stubs, pending more data or a cleaner signal. That is not a failure. That is the standard working. An overfit model in production destroys institutional trust faster than an honest gap in coverage.
Audit-first go-to-market.
The first engagement is always a diagnostic audit using the client's own data. Not a demo. Not a pitch deck. A diagnostic run that shows buyers their own blind spots in their own portfolio. The fastest path to institutional trust is not explaining how the models work — it is showing a fund manager that their current underwriting process is missing 23% of the risk in their existing book. First sale is always: "Show me my own data."
Capital validates intelligence.
Capital partners deploy real money against the same models sold to Intelligence buyers. This is the only credible validation that matters. Checking a model against what actually happened is necessary but insufficient for institutional trust. The question that separates real intelligence infrastructure from analytics theater is: does anyone have money on it? The capital side has money on it. That is not a marketing claim — it is an underwriting commitment that creates accountability no demo can replicate.