The Enterprise AI Platform runs on the same kind of operational foundation as the rest of your estate — which is what makes deploying it into your own environment practical rather than a special case.
The controls that govern AI access, the enforcement of data rules and the visibility that attributes cost all depend on the same operational foundation as everything else you run. Treating AI as a separate platform means duplicating capabilities your teams already operate, and duplicating the ways they can fail.
This is also what makes customer-managed deployment realistic. A platform that assumes its own bespoke environment is difficult to put inside someone else's compliance boundary. One built to the standards enterprises already run can be deployed into an existing estate, governed by existing identity, and monitored with existing tooling.
Where a customer's foundations need strengthening before a self-hosted deployment, we work with what is already there rather than proposing to replace it.
Runs on your cloud or private infrastructure, scaling with demand rather than provisioned for a peak that rarely arrives.
Security applied as the platform changes, rather than assessed periodically afterwards.
Environments that are reproducible, reviewable and consistent between deployments.
Operational visibility available from day one rather than added after the first incident.
Teams get what they need without waiting on a queue, which is what makes adoption happen.
Fits the identity, networking and tooling you already have, instead of asking you to change them.
A platform is judged on how many teams choose it, so the design target is the fastest path to production rather than the most complete abstraction.
Controls are built into the platform, so meeting the standard is what happens when a team does nothing special.
The same foundation that runs your services runs your AI workloads, including self-hosted model serving.
Because customers deploy the platform into their own estate, it has to work with what is already there — which is a harder requirement than working in a lab.
Because AI workloads do not run in isolation. The Enterprise AI Platform sits on the same operational foundation as the rest of your estate. Organisations that treat AI as a separate stack end up maintaining two of everything — and two of every way it can fail.
A cloud or container environment, an identity provider, and a way to deliver infrastructure change. Most enterprises already have all three. Where something is missing or inconsistent, we work with what exists rather than proposing a replacement programme.
Yes. Running open-weight models inside your own boundary is supported on the same foundation as everything else, so it does not require a parallel environment built specially for it.
AWS, Azure, Google Cloud, and private or on-premises infrastructure. Vendor neutrality is a design position, so the platform is not tied to one provider's services.
That is what the self-hosted model exists for. Infrastructure, data, identity and audit stay inside your compliance boundary, controls are applied as defaults rather than checklists, and the evidence an audit needs is produced by the platform rather than assembled by hand.
A reusable platform for developing and operating enterprise AI solutions.
Orchestration of AI-driven workflows that carry real work to completion, safely.
Search and grounded, source-cited answers across your organisation's own information.
Improve AI efficiency and keep enterprise AI spend predictable as adoption grows.
Talk to us about running the Enterprise AI Platform inside your own environment — what it needs, what it provides, and how it fits what you already operate.