Healthcare risk adjustment, anomaly detection, and the high-stakes scoring problems beyond.
Risk adjustment submissions get audited. Anomaly determinations get challenged. Care prioritization decisions affect outcomes. When scoring decisions matter, the AI behind them needs to be auditable, repeatable, and defensible — and most AI isn’t. Black-box models can’t show their work. Traditional models require labeled training data the hardest problems don’t have. MST is a hybrid AI platform built differently: every decision is fully reconstructable and defensible to regulators and auditors, the same input always produces the same result, and the system learns from your operational data without requiring labels. Currently deployed for healthcare risk adjustment and anomaly detection, with an architecture designed to extend across regulated and high-stakes domains.
Auditable | Label-free | Reproducible |
| Every decision is fully reconstructable from signals through scoring through composition. | Production accuracy without labeled training data. | The same input always produces the same result, every time. |