We support high-stakes model operations — where rollbacks must be instant, drift can’t go undetected, and compliance is a build-time requirement, not a patch.
We implement model registries, approval gates, and telemetry that connect to your CI/CD and observability stack — making drift detection and rollback a push-button task.
Our infrastructure supports 20+ models in production across FDA- and HIPAA-regulated systems — with audit logging, rollback-ready deployments, and full lifecycle governance.
We go beyond classical MLOps: prompt versioning, caching, RAG-aware evaluation, and output validation are built into the stack — enabling safe, auditable LLM use in production.
MLOps handles lifecycle automation for classical ML models — training, versioning, deployment, and monitoring. LLMOps applies those principles to generative AI systems, layering in prompt versioning, RAG pipelines, eval harnesses, and compliance-aware output control.
Yes. We've deployed pipelines in FDA- and HIPAA-regulated environments, using audit trails, rollback workflows, access gating, and policy enforcement to meet compliance without slowing down delivery.
Clients have seen up to 60% faster deployment cycles, fewer model failures in production, and higher model trust among compliance and business teams. This translates to faster time-to-value, lower maintenance overhead, and smoother audits.
We implement shared CI/CD workflows, model registries, approval gates, and observability layers — creating clear handoffs between experimentation and production, and minimizing coordination delays.
We work across open-source (MLflow, Metaflow, Kubeflow), cloud-native stacks (SageMaker, Vertex AI, Azure ML), and enterprise tools — tailoring to your infra and compliance constraints.
We configure drift monitoring with custom thresholds and alerts, tied to retraining triggers and test harnesses. Our setups support shadow deployments, model A/Bs, and rollback-safe retraining cycles.
