Designed together
Model, compute, server, storage, network and security in one architecture.
AI Infrastructure
We size GPU servers, storage, networking and model deployment to your AI workload and install the infrastructure in your own data centre, isolated from external networks where required.
End-to-End Architecture
We build the infrastructure end to end, so models, data and applications run on one architecture.
Sized to you
Capacity from today's load plus room to grow.
Security from the start
Access, isolation and logging designed in.
Private AI Infrastructure
We run AI in your own environment with an architecture that can be isolated from external networks. Access, isolation and monitoring are designed in from the start.
Isolatable architecture
Can run without any external network connection.
Data boundary
Models and data stay in your own environment.
Monitoring
Access, usage and performance are logged.
AI Compute
Inference and model-serving infrastructure on GPUs and dedicated AI accelerators.
Workload analysis
A load profile derived from the real use case.
Sizing
Accelerator type, count and memory needs.
Inference optimisation
Tuning the balance of latency and cost.
Enterprise Servers
We derive server choices from application and model needs, not from a catalogue. Redundancy, capacity and maintenance are planned around your own data centre.
Chosen by need
Servers are sized to application and model load.
Redundancy
Dual power, disk and network against downtime.
Maintenance plan
Hardware lifecycle and spares planned up front.
Storage / Network
We plan how document and vector data is stored, backed up and moved safely across your network. Segmentation and bandwidth are sized for the AI workload.
Document and vector data
Storage suited to the data AI works with.
Backup
Regular backups and tested recovery.
Network segmentation
AI traffic is separated from the corporate network.
Model Deployment
We choose the right model family and size and serve it on your infrastructure. Versioning, performance and cost tracking are part of the deployment.
Model family and size
Model choice that fits the task, language and resource limits.
Serving layer
Exposing the model to applications as an API and scaling it.
Version management
Model and prompt versions change under control.
Frequently asked questions
Can we start small?
Yes. We start at PoC scale and design in room to grow.
Who operates it?
Installation, monitoring and support scope are defined in the contract.
Can private AI run without internet access?
Depending on the deployment, we assess architectures that can be isolated from external networks.
Let’s size your AI infrastructure together.
We listen to your needs and work out the right solution and timeline with you.