Sovereign AI for Canadian firms, on your hardware or in a Canadian region, with provable residency.
One Canadian team designs the deployment, racks the hardware, and proves the residency. Private AI only earns the name when you can show where the data sat the whole time, not just where you meant it to sit.
On-premise GPU, a Canadian region with a private endpoint, and an air gapped build, all costed against the same workload before anyone signs a hardware quote.
Servers, networking, storage, and orchestration, with a self hosted LLM serving layer behind your own identity provider, so prompts and outputs stay on hardware you control.
Where the data sits, where it moves, and the network path, retention, and access documentation your auditor asks for.
Among Canadian businesses with 100 or more employees, 30.0% say privacy or security is what's limiting their use of AI (Statistics Canada, 2026). That's a deployment question before it's a model question.
IN PRODUCTION
Projects stall for a year because the board won't put records on a US-owned endpoint and nobody has priced the alternatives. Price all three against the same workload and on-premise is often the wrong answer: a rack running at a fraction of its capacity costs more than the cloud it was meant to replace. The useful thing we sell is usually the option that earns us less.
Sovereignty isn't one decision. It's three, and the cheapest of the three is usually the right one. This is written by the engineer who'd build it.
Sovereign AI means you can say where the model runs, where the data sits, and whose law could reach either. Most Canadian buyers worry about one of those three, not all, and the right build changes with which one. 19.2% of Canadian businesses now use AI to produce goods or deliver services, triple the 6.1% recorded two years earlier, and privacy or security is the leading barrier among the ones holding back. For firms with 100 or more employees that barrier hits 30.0% (source: Statistics Canada, 2026).
We price three deployments against the same workload before recommending one. On-premise GPU, a local AI deployment on hardware you own and power, when the data legally cannot leave your building or your usage is heavy enough to beat rental economics. A Canadian region with residency controls and a private endpoint when it isn't. Air gapped AI when the classification demands it. Risk sits in buying hardware first: a rack running at a fraction of its capacity costs more than the cloud you were trying to avoid, and no vendor publishes that number for you.
Ottawa is spending on this, which is shifting what buyers expect. Canada committed $2B over five years to the Canadian Sovereign AI Compute Strategy, including up to $1B in public supercomputing and up to $300M to help businesses buy compute (source: ISED Canada, 2026). Rules are firming up alongside the money. Quebec's Law 25 requires a privacy assessment before personal information moves outside the province, including to another province, and OSFI's Guideline E-23 takes effect 2027-05-01 for AI models at federally regulated institutions, whoever built them.
Four steps, and the first one stops more projects than it starts. Most sovereign AI conversations open with a hardware quote, which is backwards. We start with the constraint: which law, which contract clause, or which board resolution is actually forcing the data to stay put. Half the time it's narrower than the buyer thought, and a Canadian region with the right controls satisfies it for a fraction of the capital.
Which regulation, contract, or classification is driving this. We read the clause, not the summary, because "our data stays in Canada" and "our data cannot touch a US-controlled provider" are different requirements with very different price tags.
On-premise, Canadian region, and air gapped, costed against your real workload and your real usage. You see all three, including the one that earns us less.
GPU servers, networking, storage, model serving, identity, and monitoring. Standard engineering, plus a rollback path for when capacity assumptions turn out wrong.
Network paths, data flow documentation, retention, and access logs written up for your auditor, your board, or your insurer. Residency you can only assert isn't residency you can defend.
Answers first, including the ones that talk you out of buying hardware. An architect takes the call, not a salesperson.
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