AI computing infrastructure
Plan AI capacity around the workload, not a hardware headline.
Designed for the way you work
Running inference, fine-tuning a model and preparing data place different demands on compute, memory, storage and networking. We help define the workload first, then develop an infrastructure plan for the site, budget and operating team. On-premises, private-cloud and hybrid options are assessed against data controls and practical running costs.
A practical starting point
An organisation exploring a private document assistant can start with a limited knowledge set and a representative group of users. That pilot reveals actual memory requirements, concurrent demand and response times before a larger investment. We distinguish training workloads from everyday inference and review where data is stored and processed. The result is a capacity decision supported by the intended workload, with clear prerequisites for production and a documented expansion path.
What we deliver
01
Workload and model sizing
02
GPU and CPU capacity planning
03
storage throughput and network design
04
rack power and cooling coordination
05
isolated environments and identity controls
06
model-serving deployment and observability
07
benchmark, recovery and operator handover
A clear route from idea to operation.
A project succeeds when the software, the infrastructure and the people running it are planned together. We establish ownership and acceptance criteria before implementation, then support a controlled launch.
01
Discover the real requirement
Map users, business journeys, existing systems and constraints. Agree the problem worth solving first.
02
Design the complete solution
Define architecture, integrations, security responsibilities, infrastructure and a reviewable delivery plan.
03
Build and validate in stages
Demonstrate working increments, validate data and permissions, and test failure paths as well as success.
04
Launch, hand over and support
Prepare migration and rollback, train the team, document operations and agree the support scope.
How we validate the result
We define representative datasets, concurrency, response-time targets and acceptable resource use before selecting the final configuration. Validation includes repeatable workload tests, access boundaries, restart behaviour, backup restoration and monitoring. Hardware procurement follows an approved bill of materials and compatibility review.
Before we begin
Can we start small and expand later?
Yes, the design can define a pilot and a growth path, subject to the selected platform. We record the constraints that affect expansion: GPU memory, chassis slots, power, cooling, storage bandwidth and software licences. Facilities work and specialist electrical approvals are scoped with the responsible providers before installation.
Continue your project
Start with a clear brief
Tell us what you want to improve, your current systems, the people involved and your target launch. We will define a practical starting point, dependencies and a scope you can review.