The execution layer for governed autonomy.
As AI shifts from generating content to executing actions, the market needs a new substrate: governance enforced inside the runtime, with proof artifacts and explicit authority at the boundary.
- Structural enforcement (not “monitoring theater”).
- Proof-of-Decision as a first-class decision artifact.
- High-stakes markets where autonomous actions must be governed.
AI is becoming operational infrastructure.
The next wave of AI is not “better answers”—it is autonomous execution across tools, workflows, and real-world operations. As capability increases, so does risk.
The missing layer is governed execution: explicit authority, enforceable constraints, and decision proof.
Governance becomes the substrate.
Oversight that lives outside the system can observe outputs—but it cannot enforce invariants. ARCHAI-D moves control into the architecture so “what cannot happen” becomes structurally unreachable.
A governed execution runtime + proof layer
ARCHAI-D introduces a deterministic decision pipeline where every autonomous action is gated before execution. Decisions produce a structured Proof-of-Decision artifact that can be inspected, replayed, and audited.
Allow / deny / constrain / escalate actions before they run.
PoD binds intent, evidence, policy, constraints, and signatures.
Explicit permissions, exit conditions, and stop-rights by design.
High-stakes autonomy
ARCHAI-D targets sectors where autonomous actions carry operational, financial, safety, or compliance consequences.
- Security & SOC automation
- Healthcare workflows
- Robotics & safety-critical systems
- Enterprise agent orchestration
- Infrastructure & operations automation
What we share
We can provide an investor briefing and deeper architecture walkthrough under NDA, including:
- Core primitives and runtime enforcement model
- IP roadmap and filing strategy
- Validation plan and pilot pathways
- Product roadmap and early market strategy