From datacenter to cloud to AI infrastructure.
Cloud architecture, private and hybrid estates, Kubernetes platforms, and the compute, storage and network design underneath them.
Infrastructure is where AI ambitions meet physics. GPU capacity, storage throughput, scheduling and network design decide whether a model serves in forty milliseconds or four hundred, and whether self-hosting is cheaper than an API or merely slower.
The same discipline applies to ordinary estates: greenfield datacenter builds, hybrid environments, Kubernetes platforms, and the reliability and cost work that most organisations postpone until an outage or an invoice forces it.
Where estates go wrong
- The migration finished and the bill went up.
- Half the estate was clicked into existence and nobody owns the code for it.
- Staging does not match production, so staging proves nothing.
- One region, and the disaster recovery plan has never been rehearsed.
- Alerts fire constantly and nobody can say which ones matter.
- Identity and access grew by accretion and now nobody will touch it.
- Accelerators sit underutilised because quotas and scheduling were never designed.
Infrastructure for AI workloads
Self-hosted inference is an infrastructure problem before it is a model problem. GPU capacity, placement, storage throughput and network design decide whether a private deployment is cheaper than a hosted API or simply slower than one.
Capabilities
Cloud & platform
- Cloud architecture and landing zones
- Cloud optimisation and FinOps
- Hybrid and private cloud
- Kubernetes and OpenShift
- Containerisation and platform engineering
Datacenter & virtualization
- Datacenter architecture
- Greenfield infrastructure builds
- VMware and OpenStack estates
- Linux engineering at scale
- Capacity planning and server sizing
Core infrastructure
- Compute, storage and networking design
- High availability and disaster recovery
- Security, identity and segmentation
- Monitoring, observability and alerting
- Infrastructure as code and automation
AI infrastructure
- GPU compute and accelerator planning
- Model serving and inference platforms
- Training and fine-tuning environments
- Self-hosted and air-gapped AI
- Scheduling, quotas and multi-tenancy
Infrastructure designed for modern AI workloads — GPU compute, model serving, training and self-hosted deployment.
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