AI / MLOps
Production-grade platforms for AI workloads
From GPU clusters to model serving, on-premise, hybrid or in the cloud.
AI & ML infrastructure
Innovation through intelligence
MLOps needs a different mindset from standard DevOps. We build secure, GPU-optimised environments that take models from experiment to reliable service.
- GPU-optimised clusters with NVIDIA A100, H100 and B200
- MLOps on Red Hat OpenShift AI (Open Data Hub) and SUSE AI
- Vector databases, model registries and observability stacks
- Automated LLM training and inference environments
- Agentic AI and RAG pipelines with sandboxed, observable runtimes
What we offer
Everything your ML platform needs
Built for reliability, security and cost control.
GPU estates at scale
Automated bare-metal provisioning, NVIDIA GPU Operator and DCGM telemetry.
High-performance networking
InfiniBand and high-speed fabrics for distributed training.
MLOps platforms
Kubeflow, MLflow, Seldon and Argo Workflows on Kubernetes or OpenShift.
Secure inference
Model endpoints behind a zero-trust mesh with canary releases and circuit breaking.
Cloud ML services
AWS SageMaker, Azure ML and Databricks, integrated with your pipelines and IAM.
Governance & cost
GPU resource management, autoscaling and inference cost optimisation.
Technology
Platform experience
OpenShift AI · SUSE AI · Kubeflow · MLflow · Seldon · Argo Workflows · LangGraph · Langfuse · SageMaker · Azure ML · Databricks · NVIDIA GPU Operator
Let’s simplify your IT
Tell us about your environment and goals. We’ll come back with a clear, practical plan.