For some work, the public cloud is simply not an option. Regulation, contracts or commercial sensitivity mean the data cannot leave your control. AI can still be useful; it just needs to run another way.
AI that stays inside your boundary
We run capable models on infrastructure you control: your hardware, your network, your access rules, air-gapped from the internet if your obligations demand it. Your data never leaves your environment, every query is logged, and you own the configuration end to end. For regulated and sovereign workloads, that control is the whole point.
Cut through the private AI label
Private AI is on every vendor’s slide and means something different on each one, from a public API with a promise attached to a model running on hardware in your own rack. The label matters less than one question: can our data leave our control, and under what conditions? We make vendors answer that plainly, and we set out the full taxonomy in what private AI actually means.
What we deliver
- On-premise and sovereign hosting of open models, sized to your workload without over-spending.
- Air-gapped deployments for genuinely sensitive or classified data.
- Regulated-data workloads with the access controls, logging and review your obligations require.
- A predictable, owned cost model, rather than metered per-token bills that climb with every use.
Cloud, hybrid or on-prem
The choice is rarely all-or-nothing. The clearest way to decide is to sort your data into what could sit in a public system, what’s commercially sensitive, and what you’re legally or contractually obliged to keep under your control. Most organisations find the last group is smaller than they feared, which usually means a hybrid approach is available: keep the bulk in the cloud and move only the sensitive workloads on-premise. We work through that sorting in on-prem vs cloud AI and the local-sovereignty angle in data sovereignty for Australian AI. Many engagements begin in the cloud to prove value quickly, then move the sensitive parts in-house once the use case is real.
Governance for sensitive workloads
Running AI on your own hardware doesn’t remove the need for rules; it makes them easier to enforce. We pair private deployment with the access controls, logging and human review that sensitive data demands, kept proportionate rather than bureaucratic, along the lines of a minimum viable governance policy. You get AI you can put to work on your most protected information and still answer for. Much of this builds on our applied AI work, the grounded assistants and smaller, owned models designed to run in exactly this kind of environment.