How It Works
Five governance layers that sit between your teams and your AI providers — enforcing policy, isolating workloads, and keeping humans in control of every critical decision.
Intelligent Request Routing
Every AI request is classified at the API boundary by business purpose and data sensitivity before it reaches a model. Low-risk tasks go to cost-efficient models. Sensitive or regulated workloads are routed to appropriate providers with the right guardrails applied before execution.
Workload Isolation
Different AI workloads run in segmented zones — similar to how your network already separates guest traffic from production systems. Regulated data never shares execution context with unregulated tasks, and cross-zone access is logged and governed.
Four-Gate Human Governance
Before any critical AI action reaches production, it passes through a four-stage validation process with human checkpoints. No AI decision bypasses human review for actions classified as sensitive, regulated, or customer-facing. The gates are configurable to your policy framework.
Continuous Oversight
A supervisory control layer monitors for drift, policy violations, and anomalous behavior across your entire AI estate in real time. You see problems as they emerge — not after they've reached production or your audit team.
Tamper-Evident AI Control Log
A cryptographically linked control log records routing decisions, approvals, and execution context — suitable for internal review, customer attestation, regulatory inquiry, and litigation hold. Every log entry is independently verifiable.
Ready to see it in your environment?
The pilot is structured to deliver measurable governance outcomes within 30–60 days.