AI / MLOps
Custom AI/MLOps platforms that reach production
From GPU clusters to model serving, on-premise, hybrid or in the cloud.
AI & ML infrastructure
Production-grade platforms for AI workloads
MLOps needs a different mindset from standard DevOps. We build secure, GPU-optimised environments that take models from experiment to reliable service.
- Design and deployment of GPU-optimised clusters (NVIDIA A100, H100, B200)
- MLOps pipelines on Red Hat OpenShift AI (Open Data Hub) and SUSE AI
- Vector databases, model registries and observability stacks
- Automated LLM training and inference environments
- Lifecycle management of hybrid and multi-cloud AI platforms
What we offer
Everything your ML platform needs
Proven in pharmaceutical, public sector and large-scale AI compute environments, and in agentic AI.
GPU estates at scale
Automated bare-metal provisioning, NVIDIA GPU Operator and device plugins, DCGM telemetry.
High-performance networking
NVIDIA Mellanox InfiniBand and high-speed fabrics for distributed training.
Kubernetes MLOps platform
Kubeflow, MLflow, Seldon and Argo Workflows on Kubernetes or OpenShift.
Secure inference
Model endpoints behind a zero-trust mesh with canary releases, retries and circuit breaking.
Agentic AI & RAG
LangGraph agents and RAG pipelines with Langfuse observability, run in sandboxed runtimes with deny-by-default egress. Cloud ML (SageMaker, Azure ML, Databricks) integrated too.
Governance & cost
Compliance, GPU resource management, autoscaling and inference cost optimisation.
Technology
Platform experience
OpenShift AI · SUSE AI · NVIDIA OpenShell · LangGraph · Langfuse · Kubeflow · MLflow · Seldon · Polyaxon · ClearML · Argo Workflows · SageMaker · Azure ML · Databricks · NVIDIA GPU Operator · DCGM
Let’s work together on your next project
Tell us about your environment and goals. We’ll come back with a clear, practical plan.