The reliability score is the beginning. Beneath it lies a geometric measurement system that sees things no benchmark can.
Every AI benchmark tests whether models can pass exams. None test whether models fabricate when they don't know. Our profiling reveals which domains a model actually knows and which it confidently fakes.
When an AI fabricates medical dosages or invents legal precedents, nobody knows until a human reads it. Fabrication has a geometric shape. We can see it at every token.
The strongest signal happens while the model reads your question — before a single word is generated. Fifteen times more signal. We gate it and prevent fabricated responses from ever being generated.
A provider pushes an update. A fine-tune shifts behavior. Your model was reliable last month — is it still? Continuous measurement makes drift visible the moment it happens.
When safety training activates, the probability distributions look fundamentally different from both normal operation and fabrication — 235 times more texture in the geometry.
From output probabilities alone, we reconstruct which concepts the model is weighing. Regulators asking “why did the AI say that?” get an answer without opening the black box.
When organizations deploy multiple AI models, correlated failures compound. If three models share the same blind spot, the system has synchronized risk, not redundancy.
The same measurement that distinguishes knowledge from fabrication reveals a continuous spectrum — from pure fabrication to verbatim reproduction.