ArcKernel is the governance substrate for AI in regulated production.
As AI systems become persistent operational entities, the organizations deploying them inherit obligations the model itself cannot satisfy: identity, trust, evidence, and continuity at machine speed. ArcKernel provides that infrastructure.
Deterministic runtime enforcement from outside the inference channel. No model access, no training-data dependency, model-agnostic across Claude, GPT, Gemini, Mistral, Grok, and open-source models. Single HTTP call.
The architectural finding behind ArcKernel is simple: governance must operate outside the inference channel. AI systems can reliably detect integrity failures but cannot reliably correct them from within the same channel that produced them. ArcKernel separates governance from inference. Detect, halt, escalate. A system prompt is a request. ArcKernel is an invariant.
Three independent forces are converging on the same infrastructure layer. Regulation is making governance required (EU AI Act, DORA, SR 11-7). Insurance is making governance underwritable. Economics is making governance operationally rational. One substrate satisfies all three.
Regulated industries require independent audit. Financial institutions cannot self-certify. Legal systems cannot self-adjudicate. The same way regulated industries cannot self-audit, AI cannot self-govern. ArcKernel is the missing institutional layer.
Enterprise pilots: pilot@arckernel.ai
Investor inquiries: investors@arckernel.ai

ArcKernel Inc.

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