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.
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.
Causal ordering, nanosecond precision
Per SEC, per MiFID II
Cryptographic cache salts
Cryptographically signed
Three products, one governance model — from training to inference to monitoring.
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.
Each framework requires evidence, audit trails, and demonstrable controls — all from one platform.
Model lineage for trade decisions. Per-request token accounting. Best execution evidence. Market abuse detection with causal audit trails.
Clinical AI explainability. Post-market surveillance. De-identification and differential privacy. Every clinical AI decision replayable for FDA audit.
Air-gapped AI deployment. Per-tenant isolation at cache-salt level. Quantum-resistant signing. No phone-home, no cloud dependency.
High-risk AI system documentation. Conformity assessment. Human oversight. Data governance and bias detection. Right to explanation.
Six governance dimensions — all native, all auditable, all on one platform.
Three products, one governance model — from training to inference to monitoring.
Governed inference — causal audit trails, per-tenant isolation, token accounting, model lineage. OpenAI-compatible API. Drop-in vLLM replacement.
Model lifecycle — training provenance, validation evidence, bias checks, approval chains. Same deployment gates for models and containers.
Real-time AI monitoring — model drift, inference latency, guardrail violations, GPU health. Anomaly detection on model behavior.
Architecture to delivery
Map your operating constraints with our solutions team and define the right delivery path.