Compact edge appliances
Low-power inference where the work happens — an office node, a site cabin, a vehicle.
AI infrastructure
AI infrastructure, from workload back to rack.
The machine comes last. First: which model, how many users, how fast, and can the data leave the building? Then we design, source and install exactly what those answers call for — anything from a single office node to a racked, air-gapped system.
The method
Sized by the workload, not the spec sheet.
A bid desk pricing from delivered-job history needs a different machine than a site analysing camera feeds — and neither should be sold a datacentre rack to find out. We size from the documents, the users, the response times and the data boundary.
Economics is part of the sizing: steady, high-volume inference usually justifies owned compute, while bursty or occasional work is often cheaper in the cloud, where policy allows it. We run that arithmetic in the audit, before anything is bought.
What we deploy
From an office node to an isolated rack.
NPU and GPU inference systems
Dedicated machines matched to the model and the concurrency, from a single desk to a shared workload.
On-premise AI servers
Sustained inference inside your network — under your roof and your controls.
Rack and isolated builds
Rack-mounted deployments, and air-gapped builds for the records that must not touch a network path.
Supporting compute, storage and networking
The rest of what production AI needs — retrieval storage, networking, backup and monitoring.
Sourcing & supply
Scoped, quoted and ordered with you.
Design, configuration and architecture are delivered as a service today. Sourcing and supply run through the same engagement — scoped, quoted and ordered with you, with access to specialist AI compute through established enterprise technology supply chains.
Vendor-independent.
No allegiance to one cloud, one chipmaker or one model family. Parts are chosen on fit and stay replaceable when something better arrives.
Infrastructure-aware.
Recommendations are made to be installed — power, heat, racks and networks are part of the spec we deliver.
Workload-led.
The machine is sized by what the work does: documents, users, concurrency, response time.
Models, chips and runtimes will keep changing. We design so those parts can be swapped without rebuilding everything that depends on them.
The engagement
Four to six weeks. One workload. The right machine.
The engagement starts with an audit that sizes the workload. If the numbers justify owned compute, we design, source and deploy it; if they don't, you know before anything is bought.
Map · Inference Audit
One workflow, mapped end to end, and a straight answer: where AI belongs, and where it doesn't. The map is yours either way.
Prove · Governed Pilot
Four to six weeks. A working agent in the real workflow, measured against a baseline agreed up front. At the end, a number — and the keep-or-stop call made on it.
Place · Private Deployment
The proven agent moves to production — on-premise, the airon appliance, or approved cloud where you allow it, decided by what its records require. It runs alongside the AI you already use.
Run · Managed Operations
After go-live we keep watch on quality, cost and model behaviour as the work changes. The audit record stays current, ready for your clients to inspect. When the numbers support it, the next workflow gets mapped.
The first conversation is about the work, not the technology. If an audit is the right next step, we scope one. If it is not, we say so.