The regulated tier. Sovereign inference with the security and data residency wrap for financial services, healthcare and critical infrastructure, designed for organisations whose data cannot leave and whose regulator is watching. It is the segment the large vendors serve worst, because the wrap is advisory work, and advisory is what we do.
Designed for
Financial services, healthcare and critical infrastructure, where the regulator, the data and the risk register all say the public API model is a non starter.
Models served entirely inside your estate, with data residency you can state in a sentence and prove in an audit.
Document Q&A and retrieval where the source material is exactly the data that must never leave: case files, patient records, customer accounts, operational systems.
Logging, access control and model governance designed in from the start, so who asked what of which model is a record, not a mystery.
Not designed for
Unconstrained workloads. If your data can move within normal enterprise controls, the Inference Pod serves the same workloads at lower cost. Buying the sovereign wrap you do not need is oversizing with a compliance accent, and we will say so.
Foundation model training. As with every tier: different discipline, different economics, almost never the real requirement. Sovereignty applies to serving and retrieval, and that is what this design carries.
Regular fine tuning at scale. Where a regulated estate also needs recurring adaptation on its own data, the sizing conversation combines this wrap with Fine Tuning Pod capacity, honestly sized for both.
Logical architecture
What is in the box
The same inference discipline as tier 1: enterprise rack servers from our Dell and HPE lines with inference class GPUs, sized by model, quantisation and concurrency.
Fast flash for models and vector indexes with encryption at rest as standard, and capacity tiers for the sensitive document estate feeding retrieval.
Network segmentation with air gap options, identity integration with your existing directory, and deployment patterns that keep every token inside your estate.
Hardened serving stack, full audit logging of model access, and controls aligned with your existing security estate, including the Mimecast and human risk tooling many of our clients already run.
No part numbers and no prices on this page, deliberately. The specifics are produced in the sizing engagement, configured to order through our Dell, HPE and distribution relationships, and integrated with the security estate you already run. The advisory behind this tier is Private AI for Regulated Organisations, and the controls build on our wider cybersecurity practice.
Sizing signals
The Private AI Pod fits when the constraint sounds like this:
Lifecycle
Every pod is wrapped by the IDEAL framework.
Sizing, procurement, deployment, adoption, then refresh and expansion managed proactively. In this tier the lifecycle also carries the governance: controls reviewed as models change, evidence kept current for the people who will ask.
Questions
Infrastructure that runs AI models entirely inside your own estate, so prompts, retrieved documents and outputs never leave your control. In practice that means serving open weight models on your own hardware with retrieval over your own data, wrapped in the isolation, identity and audit controls a regulated organisation already applies to its most sensitive systems.
Yes. Open weight models now cover most enterprise tasks well, and served on premises they keep every prompt and document inside your estate. The engineering is well understood: the real work is sizing honestly, wrapping the deployment in proper controls, and proving residency to the people who will ask. That is what this design exists to do.
AI capability under your jurisdiction and control: models running on infrastructure you own or control, in a location you choose, governed by your policies rather than a provider’s terms. For most organisations it is less about nationalism and more about a plain question from a regulator or customer: where exactly does this data go? Sovereign deployment lets you answer in one sentence.
The workloads are the same, inference and retrieval, so the decider is constraint, not capability. If your data can move within normal enterprise controls, the Inference Pod is the economical choice. If residency, regulation or contractual terms are the design constraint, the Private AI Pod builds the wrap in from the start, which is materially cheaper and more credible than retrofitting it.
No published price, deliberately. The number depends on the sizing and on how much of the security wrap your estate already provides, since we integrate with existing controls rather than duplicating them. The sizing conversation produces a validated bill of materials configured to order through our Dell, HPE and distribution relationships.
Tell us the constraint and the workload. We will design the inference estate that satisfies both, and tell you honestly if a simpler tier does the job.
Book a sizing conversation