Secure, reliable production AI infrastructure.
We build and operate the platforms your AI runs on — GPU Kubernetes, MLOps pipelines, DevSecOps, and SRE — for teams shipping models to production. 20+ years across Tier-1 banking and healthcare AI.
Three pillars of production AI infrastructure
Production GPU Kubernetes, KServe + Knative + Istio serverless serving, MLflow registry promotion, drift monitoring, and automated retraining pipelines.
CI/CD platforms with reusable workflow libraries. SBOM, image signing, Zero Trust IAM, privileged access — security gates from commit to production.
Multi-cloud K8s on AWS and Azure, GitOps delivery, observability platforms, SLO programs, and vulnerability management at fleet scale.
Featured case studies
Healthcare AI client running real-time RAG on EKS was burning ~$170–180K/month in idle GPU and over-provisioned compute. We traced and remediated 70% of unallocated spend.
End-to-end MLOps stack for real-time RAG inference at a Fortune 100 healthcare AI program — full lifecycle from experiment tracking to canary rollout on drift.
Tier-1 retail brokerage replaced legacy Harness CI/CD with GitHub Actions across 5,000+ Linux/Windows servers — reusable workflow library, OIDC-federated runners, security gates as required checks.
Latest writing
- Where 70% of EKS spend hides: a 5-step GPU FinOps auditHow to find the unallocated GPU and compute spend that cost dashboards can't see — and what to do about it.
- Why we bake model.pkl into Docker images instead of pulling from MLflow at runtimeMLflow is great for experiment tracking and registry. It's not great as a runtime dependency for production inference pods.
- Champion / challenger model promotion that doesn't break inference SLOsA safe-by-default pipeline for promoting models in production: alias-based rollouts, evaluation gates, and canary traffic splits.
Got a hard production AI problem? Let's talk.
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