Private & On-Premise AI
We assess the workload, model, infrastructure and integrations, then deploy the approved design on your servers or private cloud. External AI calls can be disabled when the selected components support fully private operation.
Deployment fit
More control. More to operate.
Private or on-premise AI can reduce external data transfers and provider dependence. It also adds capacity planning, monitoring, updates and model operations. We compare public cloud, private cloud and on-premise before recommending it.
Configured example. Actual network behaviour depends on the approved architecture and integrations.
What we deliver
Deployed in your data centre or private cloud — your infrastructure, your rules.
Keep selected AI processing in your chosen environment and reduce reliance on external providers. Your wider GDPR and governance obligations still apply.
For steady, suitable workloads, your own infrastructure can make capacity and operating costs easier to plan than usage-based cloud pricing.
Run selected automations and agents on-premise, sized for the model, workload and performance target.
How it works
We test models that can run in your environment, select the right fit and connect the chosen model to your systems.
Data and model stay inside the defined environment. External AI calls can be disabled.
What you buy
We assess feasibility and risk, build a focused pilot, then prepare only an accepted solution for production. Every stage has agreed deliverables and acceptance criteria.
We map the process, data flows, integrations, security requirements, operating model and result to measure.
A process map, data-flow and risk review, solution options and a fixed proposal for the next stage.
We build one use case in an agreed environment and evaluate quality, controls, performance and operating effort against the acceptance criteria.
A working pilot, test results, known limitations and a clear recommendation on whether to proceed.
We prepare the accepted solution and agree release, monitoring, rollback, support and operating responsibilities.
A production release and the agreed handover assets: source code and settings, deployment automation, monitoring and operating instructions.
FAQ
It depends on the task. We compare answer quality, the amount of information the model can handle, response time, hardware and operating effort. Then we test the best candidate against your acceptance criteria.
It depends on the workload — we size it during discovery, from a single server up.
It can support data-residency and supplier-governance requirements by reducing external transfers and sub-processors. Deployment location alone does not make processing GDPR-compliant; your lawful basis, access controls, retention and governance obligations remain.
Yes, where the chosen models and components permit it. We design for portability and document any third-party dependencies before the build.
A focused prototype can often be ready within weeks. Production timing depends on scope; after discovery, the fixed proposal sets concrete dates.
When rapid access to the largest hosted models, low initial infrastructure effort or highly variable usage matters more than isolation. We recommend it only after comparing security, quality, response time, capacity, operating effort and total cost.
Contact
Describe one process or task, or a data or security constraint. Two sentences are enough. We usually reply within one business day.
Or directly:
A selection of organisations we have worked with
