§ Arkheia · The Science
Mathematical certainty
in AI governance
Most AI evaluation asks “does this look right?” Arkheia asks “is this statistically consistent with how this model normally behaves?” The difference is measurable, defensible, and auditable.
§ How It Works
The detection pipeline
01
Characterise
Build a behavioural profile for each model family from controlled characterisation runs. Not generic thresholds — empirical data.
02
Observe
Intercept every invocation at the API boundary. Extract surface signals: token statistics, timing patterns, output structure.
03
Compare
Calculate effect sizes against the model's behavioural baseline. Cohen's d > 0.8 = actionable drift.
04
Decide
Three-zone classification: Safe, Review, Hold. Execution decisions, not confidence scores. Honest about uncertainty.
See the science
in action
Detection profiles built from empirical data. Per-model baselines. Statistical rigour you can explain to your auditor.