The data cannot leave
Legal and procurement will not sign off on sending customer records, contracts or patient data to a third-party API. Workarounds appear quietly, outside any policy.
Private AI infrastructure
Your models. Your data. Your perimeter.
We plan, deploy and operate dedicated LLM environments for organisations that cannot send their data to a public API - in your cloud account, in your datacentre, or in single-tenant capacity that we run for you.
- Single-tenant by default
- EU/EEA data residency
- Fixed monthly retainer
Sectors we work with
Financial services
Healthcare
Legal
Manufacturing
Logistics
Why programmes stall
Three problems stop most AI rollouts. All three are infrastructure problems.
The model is rarely the blocker. What blocks a rollout is where it runs, what it costs and who is left holding the pager afterwards.
Cost is unforecastable
Per-token billing scales with success. Finance cannot approve a budget line that grows every time the product becomes more useful.
Nobody wants to own the GPUs
Platform teams are already stretched. A new inference stack means new drivers, new failure modes and a new on-call rotation.
What we do
One contract for design, deployment and operations.
We take responsibility for the whole stack between your users and your data. You keep the applications and the business logic.
Dedicated inference
Open-weight models served on single-tenant GPU capacity, sized for your latency and throughput targets rather than a generic benchmark.
Private retrieval
Grounding over your own documents, with access control inherited from the source system instead of copied into a vector store.
Identity and access
SAML or OIDC single sign-on, group-to-role mapping and per-team quotas enforced at the gateway, not in the prompt.
Audit and observability
Request-level logs, retention you control, and cost attribution per team and per application - exportable to your existing SIEM.
Model lifecycle
Evaluations, upgrades and rollbacks. A new model is benchmarked against your workload before anything reaches production.
Managed operations
Twenty-four hour monitoring, patching and incident response, with a named engineer who knows your deployment by name.
How we work
Four phases, fixed scope, no surprises.
Every phase ends with a document you can take to your security team, your finance team or your board.
Scoping
Week 1. Workloads, data classification, threat model, latency and throughput targets, budget envelope.
Design
Weeks 1-2. Reference architecture, capacity plan, integration points, acceptance criteria and a written cost model.
Deployment
Weeks 2-5. Build, harden, integrate, load test and hand over. Your engineers are in the room throughout.
Operations
Ongoing. Monitoring, patching, incident response and a quarterly capacity and cost review.
3-6 weeksTypical time to the first production workload
Single-tenantDefault deployment model, no shared inference
99.9%Availability target on managed engagements
EU/EEAData residency options, including your own datacentre
Security is part of the product, not an add-on.
Data classification, network isolation, key management, retention policy and audit trails are agreed before a single GPU is provisioned. We support your review with architecture diagrams, a completed vendor questionnaire and penetration-test coordination.
Questions
Frequently asked
Do you train on our data?
No. We do not train, fine-tune or evaluate models on customer data unless it is a separate, explicitly scoped engagement with its own contract. Inference traffic is processed and discarded according to the retention policy agreed during scoping.
Where does the infrastructure run?
Three options: inside your own cloud account, inside your datacentre on hardware you already own, or in a dedicated single-tenant environment that we operate in EU datacentres. The site remains yours in all three cases.
Which models do you work with?
Mainly open-weight families - Llama, Mistral, Qwen and Gemma - plus commercial models through private endpoints where the licence permits it. Model choice follows from your workload, language coverage and hardware budget, not from a vendor relationship.
How is this priced?
A fixed deployment fee, a monthly operations retainer and infrastructure at cost. We do not mark up GPU or cloud spend, and we do not take a percentage of your usage. Ranges are published on the services page.
We already have GPUs. Can you work with them?
Yes, and it is usually the cheapest route. We deploy into your existing cluster, work within your storage and networking constraints, and document what would need to change if you outgrow it.
How do you handle a security review?
We complete your questionnaire, join architecture reviews and provide the network and data-flow diagrams you need. If a penetration test is required before go-live, we scope and coordinate it with your team.
Tell us what you are trying to ship.
Send a short description of the workload, the data classification and your timeline. You will get an honest assessment of whether we are the right fit - usually within one business day.