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MLOps & LLMOps

From “cool demo” to production GenAI — with SLOs and a budget.

The prototype impressed everyone. Now you need model serving that scales, RAG that stays accurate, evaluations that catch regressions and GPU costs that don’t torch the budget. That is a platform problem, and it is what we do.

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What you get out of it

  • GPU-backed Kubernetes with autoscaling and scale-to-zero — no idle GPU burn
  • Model serving with real SLOs: latency, throughput and availability targets met
  • RAG pipelines with versioned data, embeddings and retrieval quality metrics
  • Evaluation harnesses and guardrails so model updates never surprise you

What we deliver

  • AI platform architecture on AKS/EKS with GPU node pools and quota governance
  • Model serving stack (vLLM, KServe, Ray Serve) with CI/CD for models
  • RAG pipeline build-out: ingestion, vector store, retrieval and re-ranking
  • Experiment tracking and model registry (MLflow, Azure ML, SageMaker)
  • Token/inference cost dashboards and budget enforcement

Tools we work with

Azure OpenAIAzure AI FoundryAWS BedrockSageMakervLLMKServeRayMLflowKubeflowpgvectorPinecone

Where we’ve done this before

Multi-national enterprise

GenAI platform on GPU-backed AKS with cost control built in

Production GenAI and agentic workloads with full audit trails, evaluation gates on every model change, and FinOps controls that eliminated idle GPU burn and capped token spend per workload.

Ready to fix mlops & llmops?

Start with the free audit — findings and a prioritized plan in five business days, no strings attached.