Compliance
Compliant by design — not bolted on.
The laws now governing AI risk demand transparency, human oversight, and traceable rationale. A named-rule trace and identifier-free inputs are exactly what rAIson already produces.
What the architecture already does
Oversight, explanation, and reproducibility are structural — not features.
Human oversight
An expert reviews the encoding unit by unit before deployment — the model proposes, the expert approves, then the engine commits. Errors are caught in the source, not the consequences.
Intelligible explanation
Every decision is the rule trace itself: the rules that fired and the counter-rules they defeated. The explanation is not generated after the fact — it is the reasoning.
Identifier-free inputs
The engine reasons over structured signs and codes, never personal identifiers — keeping decisions auditable while minimising the data they touch.
The regimes
Mapped to the laws governing high-risk AI.
Transparency and human oversight
High-risk AI must give an intelligible explanation for every output, and a human must be able to review and override every decision. A black box cannot satisfy this; a named-rule trace can.
Lifecycle management of medical software
Predictable behaviour, documented updates, and a traceable rationale for every clinical recommendation. Editable rules and signed updates beat opaque retraining cycles.
Automated decisions and the right to explanation
The architecture transmits only structured signs and codes — never identifiers — and produces explanations that are the rule trace itself.
Medical devices and protected health information
Quality-management systems, post-market surveillance, and Safe-Harbor-grade de-identification — delivered by auditable updates and identifier-free inputs.
Compliance is not an add-on layer. It is what the architecture was already doing to produce reproducible, explainable, defensible decisions.
Decisions you can take to an auditor.
See how a named-rule trace satisfies the regimes that apply to your domain.
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