AI Governance & Compliance
Deploy AI that your compliance team, your auditor, and your CFO can all sign off on. Prometheus governs inference, Haephestus governs model lifecycle, and Lynceus observes every prediction in real time.
The AI Governance Gap
If your AI cannot explain itself, you do not have governance
Most AI infrastructure was built for research, not production. When an auditor asks why a model made a specific prediction, the answer from a Python-based inference engine is 'the vector store returned something.' When a regulator asks for model lineage, the answer is a manually compiled report that takes two weeks.
AI governance requires infrastructure that was built for it from the start — not bolted on after the model is already in production. Every inference must be causally traced, cryptographically signed, and regulator-ready. Per-tenant isolation must be enforced at the infrastructure level, not the application level.
Experience that grows with your scale
Inference Events Traced
Causal ordering, nanosecond precision
Experience that grows with your scale
Model Lineage Retention
Per SEC, per MiFID II
Experience that grows with your scale
Per-Tenant Isolation
Cryptographic cache salts
Experience that grows with your scale
Audit Trail Integrity
Cryptographically signed
The AI Governance Stack
Three products, one governance model — from training to inference to monitoring.
- Prometheus: Inference GovernanceEvery inference event recorded at nanosecond precision with causal ordering — model version, adapter selection, retrieval context, tool dispatch, decision boundary. Cryptographically verifiable.
- Haephestus: Model LifecycleModel versioning with the same rigor as container images — signed, scanned, provenance-tracked. Training data, validation results, bias checks, performance metrics. No model reaches production without documented approval.
- Lynceus: Real-Time MonitoringModel drift, inference latency, token cost, guardrail violations, and GPU health — monitored in real time. Anomaly detection on model behavior, not just infrastructure metrics.
- GEOM-db: Data LineageTraining data provenance stored with Merkle integrity. When a model is audited, the full data lineage is available — which data, which version, which consent, which transformation.
From Training to Production
An experiment produces a model. The model carries its experiment provenance — training data, hyperparameters, code version, environment, metrics — into the registry. When the model is promoted from staging to production, it goes through an approval chain with evidence.
When a regulator asks why a model made a specific decision, the full chain — from training data through experiment to deployment to inference — is one query away. Not a two-week report compilation.
Compliance Frameworks
Each framework requires evidence, audit trails, and demonstrable controls — all from one platform.
Financial (SEC, FINRA)
Model lineage for trade decisions. Per-request token accounting. Best execution evidence. Market abuse detection with causal audit trails.
Healthcare (FDA, HIPAA)
Clinical AI explainability. Post-market surveillance. De-identification and differential privacy. Every clinical AI decision replayable for FDA audit.
Defense (ITAR, NIST)
Air-gapped AI deployment. Per-tenant isolation at cache-salt level. Quantum-resistant signing. No phone-home, no cloud dependency.
EU (EU AI Act, GDPR)
High-risk AI system documentation. Conformity assessment. Human oversight. Data governance and bias detection. Right to explanation.
AI Governance Capabilities
Six governance dimensions — all native, all auditable, all on one platform.
The AI Governance Stack
Three products, one governance model — from training to inference to monitoring.
Prometheus
Governed inference — causal audit trails, per-tenant isolation, token accounting, model lineage. OpenAI-compatible API. Drop-in vLLM replacement.
Haephestus
Model lifecycle — training provenance, validation evidence, bias checks, approval chains. Same deployment gates for models and containers.
Lynceus
Real-time AI monitoring — model drift, inference latency, guardrail violations, GPU health. Anomaly detection on model behavior.
AI Governance & Compliance FAQ
Architecture to delivery
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