A model loses coherence before it loses the truth.
A model that’s about to hallucinate doesn’t announce it — but its internal coherence slips first, while the text it’s producing still reads fluent and confident. EIDOS measures that internal slip directly, rather than grading the output after it’s written.
This is the part worth being clear about: Snapmatics isn’t another model. It’s the physics that watches one — substrate-independent, the same coherence law we use on a heart or a bearing, here pointed at a model’s own behavior. It reads drift toward error and recovery back to stable alike.
A coherence read on a model’s own state — catching drift before the output does.
One framework. The governance instrument it becomes.
AI Behavior isn’t a separate product — it’s Snapmatics, pointed at a model’s own behavior. The same four layers take a specific form here.
Reads internal coherence
Computes how well a model is holding its own internal state — the measurement EIDOS acts on.
Locates where drift starts
Identifies where in the model’s behavior coherence begins to slip, not just that it did.
Gates the intervention
The layer this vertical is built on — governs and gates, catching drift toward hallucination before the output is trusted.
States what drifted
Where licensed, EVE reports what slipped and when, in plain language for the team running the system.
Bring us your hardest signal. We’ll show you its tell.
Model builders and AI-safety teams: explore an early-access evaluation of EIDOS on your own system under NDA.