Your data cannot leave, your regulator is watching, and the public API model is a non starter. That does not put AI out of reach. Private inference brings the models to the data: open weight models served inside your estate, retrieval over your own documents, and a security wrap your auditors can actually inspect. It is the segment the large vendors serve worst, and the one where independent design matters most.
The problem
Every AI capability your competitors are adopting arrives, for you, attached to a question: where does the data go? For a regulated estate the honest answer to the public API version is usually somewhere we cannot fully evidence, and that answer fails the audit.
The organisations that solve this do not wait for the constraint to lift. They run inference where the data already lives, under controls they already operate, and adopt AI at full speed inside them.
What it covers
Open weight models served entirely inside your estate, sized to your workload, so prompts, documents and outputs never leave your control.
Deployment patterns that let you answer the regulator’s question in one sentence: the data stays here, and here is the evidence.
Which open models genuinely cover your tasks, at what size and quantisation, chosen by evaluation rather than by headline.
Isolation, identity, audit logging and acceptable use controls, integrated with the security estate you already run rather than duplicating it.
The architecture behind this page is the Private AI Pod, our validated design for sovereign inference, and the wider controls come from our cybersecurity practice. We work with organisations in telecoms, financial services and critical infrastructure, where these constraints are daily reality rather than edge cases.
Questions
AI capability, usually large language model inference and retrieval, run entirely within your own infrastructure and control, rather than through a public API. For regulated organisations it is the difference between adopting AI and adding an unapproved data processor: the models come to the data, the data goes nowhere.
For many workloads an enterprise API agreement is fine, and we say so plainly. It stops being fine when regulation, client contracts or your own risk appetite mean sensitive data cannot transit a third party at all, or when you cannot evidence where inference happens to the people entitled to ask. At that point private inference is not the expensive option, it is the only compliant one.
Honestly: the largest frontier models are more capable than anything you can host. The real question is whether your tasks need them, and evaluation usually shows that retrieval over your own data with a strong open weight model covers the enterprise workload well. We test that claim against your tasks before you spend, and if your use case genuinely needs a frontier model, we will tell you and help you get there safely.
Financial services, healthcare and critical infrastructure lead, because regulation and data sensitivity combine, but the same constraint appears anywhere client confidentiality or contractual data terms bind: legal, defence supply chains, public sector. If your data classification policy would block staff pasting the content into a public tool, that content needs a private path to AI.
Less than the vendor pitch suggests. A sizing conversation establishes the workload, the Private AI Pod validated design gives the architecture, procurement runs through our Dell, HPE and distribution relationships, and the security wrap integrates with your existing controls. The result is an inference estate your infrastructure team can run, not a research project.
Tell us the regulation, the data and the workload. We will design the private inference estate that satisfies all three, and tell you honestly where an enterprise API agreement would serve you just as well.
Talk to us about private AI