AI that runs inside your own network.

Almost nobody asks for on-premise hardware because they want hardware. They ask because they want a defensible answer to a different question: where does our material go, who can reach it, and what happens when a client or a regulator asks us to account for it.

On-premise is one answer to that question. It is a good one, it is not the only one, and it is not free. Worth knowing what it actually buys before it becomes a line in a procurement document that nobody can trace back to a rule.

A managed Cogneros appliance is installed in your office or data centre. Approved models run locally, the index is built and held inside your network, and egress is restricted to what your policy allows — which can be nothing at all for the categories you define.

The appliance is managed. It is kept ingesting, patched, monitored and supported the same way a cloud environment is. On-premise means the hardware is yours and the work stays inside it; it does not mean you inherit an operations burden along with the rack space.

Models that run on hardware you own are smaller than the frontier models that run in a data centre. For retrieval and citation over your own material that gap matters far less than people expect, because the hard part is finding the right passage rather than composing the sentence around it. For open-ended reasoning, the gap is real.

Capacity becomes a decision rather than an elastic default. You size for the work you expect, and adding capacity later means adding hardware rather than changing a setting.

Both of these are properties of local inference rather than of any one vendor. Anyone who tells you on-premise costs nothing is selling you the hardware.

It is where the conversation usually lands once the question gets specific. Defined categories of work stay on hardware inside your network; everything else runs in a private cloud environment, where capability is highest and there is nothing to rack.

You set the boundary — by matter, by client, by role — and you can move it later. Many organizations start entirely in the cloud and pull categories local as the genuinely sensitive work becomes clear, which is a much better order than guessing at the start.

Cogneros is model-flexible. Work can route to local models, to approved cloud models such as Claude or GPT, or to a combination. Which models are available, to whom, and for which kind of work is configured by you.

That is usually the part that survives a security review: not a promise that nothing ever reaches a cloud model, but a written, enforced rule about which work may reach which model — and a log showing the rule held.