We design governance that doesn't just pass policy review — it holds up in pipelines, shows up in audit logs, and earns buy-in across legal, risk, and engineering.
LLMs need prompt tracing. Classification models need bias thresholds. Clinical models need human override paths. We don’t apply templates — we architect controls based on model function, risk class, and user impact.
Our governance patterns are built for real-world delivery: approval queues that don’t block workflows, policy engines that integrate with CI/CD, and monitoring that doesn't drown teams in alerts.
We’ve enforced guardrails across 40+ production models in regulated domains — including healthcare and pharma — where explainability and access control aren’t optional, and downtime isn’t tolerated.
AI governance refers to how model risks, outputs, and processes are controlled, reviewed, and documented. It’s essential for compliance, transparency, and production trust.
LLMs require prompt traceability, output filters, and hallucination checks — which we layer on top of classical MLOps pipelines.
Yes. We use CI/CD hooks, policy-as-code, and streamlined reviewer flows — ensuring delivery velocity with no compliance tradeoff.
Lack of runtime enforcement. Most orgs rely on documentation and manual checks instead of embedding controls into the actual delivery flow.
Yes — we’ve built pipelines with end-to-end lineage, policy enforcement, and audit support across regulated healthcare and finance teams.
Book a quick audit of one model or pipeline — and we’ll map gaps, propose controls, and scope an implementation path.
