AI infrastructure and deployment
We support both on-premise and cloud-native AI deployments, depending on security, latency, compliance, and cost requirements.
On-prem GPU cluster design
We design and deploy dedicated GPU infrastructure for organisations that need maximum control over their AI workloads. This includes hardware selection, rack layout, networking, cooling, and software stack configuration — optimised for ML training, inference, or both.
Hybrid AI architectures
Most production AI systems don't live in a single location. We design hybrid architectures that distribute inference and training across on-prem, edge, and cloud — balancing latency, cost, data residency, and redundancy.
Cloud deployment on Azure, AWS, or GCP
We deploy and optimise AI workloads on all major cloud platforms. This includes selecting the right GPU instances, configuring auto-scaling, managing costs, and ensuring your models run efficiently at production scale.
NVIDIA AI Enterprise and NIM deployments
As an NVIDIA partner, we deploy production AI using the NVIDIA AI Enterprise platform and NVIDIA NIM microservices. This gives organisations access to optimised, enterprise-grade inference for LLMs, vision models, and speech models — with full support and security.
VM setup, GPU passthrough, and resource isolation
For organisations running shared infrastructure, we configure secure GPU virtualisation with proper isolation. This means multiple AI workloads can share physical hardware without interfering with each other — critical for multi-tenant or regulated environments.
Performance optimisation for AI workloads
We profile and optimise the entire AI stack — from model compilation and quantisation to memory management, batching strategies, and throughput tuning. The goal is maximum inference speed at minimum cost.
Air-gapped and sovereign AI deployments
For defence, government, healthcare, and financial organisations that cannot send data to the cloud, we build fully air-gapped AI deployments. Everything runs on-premises with no external connectivity — models, data, inference, and management tooling.