On-premise and sovereign AI for data that can't leave Canada

Talk to an architectSee what's covered

Trusted by

  • Broadcom
  • SSENSE
  • Mondou
  • Western University
  • Votatec
  • Ren's Pets
  • City of Mississauga
The work

On-premise and sovereign AI, three pillars, one operator.

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.

Contact us

  1. Deployment design

    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.

  2. GPU and platform build

    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.

  3. Residency and evidence

    Where the data sits, where it moves, and the network path, retention, and access documentation your auditor asks for.

By industry

Does On-Premise and Sovereign AI work the same way in every industry?

No. The On-Premise and Sovereign AI work is the same craft in every sector, but the conditions around it are not. In government that means Protected A, B and C data handling; in defence and aerospace, controlled goods rules, CMMC and the flow-down from your prime. We keep the service and change the part that bends.

  • For data that cannot leave Canadian soil, whatever the vendor promises.

    Protected A, B and C. Each level changes where the data can physically live, who can reach it, and what you have to prove about both. Most files turn on Protected B, and most disagreements turn on what counts as reaching it.

    • ITSG-33, and ITSP.10.033
    • CPCSC
    • PIPEDA and the Privacy Act
    Government IT services

  • Controlled data that can't cross a border, including into a model.

    ITAR and the Controlled Goods Program. Registration under the Defence Production Act is mandatory before anyone examines, possesses or transfers controlled goods in Canada. Technical data counts, so a drawing sitting on a laptop is in scope.

    • CMMC
    • NIST SP 800-171
    • CPCSC
    Defence and aerospace IT
Request a consult

What do you need from On-Premise and Sovereign AI?

Tell us who you are and what you're working on. A senior engineer reads your message before replying, so the first answer is a real read on scope, risk and timing, within an hour, 24/7. Nothing here is a commitment, and we'll tell you plainly if we're not the right fit.

I agree to receive emails from SMEnode.

We use your details to reply to this message. You can unsubscribe from our emails at any time. Privacy

Your request

Service
On-Premise and Sovereign AI

What happens next

  1. A senior engineer replies within an hour, 24/7, never an SDR.
  2. We book a 30-minute call to listen, sketch options, and flag risks.
  3. You get a scoped statement of work within 3 business days.

Call the 24/7 line

+1 514-465-1584
The deep dive

Data sovereignty in Canada, made real.

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.

What sovereign AI actually means.

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).

Three deployments, priced against one workload.

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.

Where the money and the rules are going.

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.

Want a Senior IT Consultant's Read on Your Setup?

Tell us what you run today and what keeps breaking. A senior SMEnode engineer replies within an hour with a first read on scope, risk and timing. No sales handoff.

Request a consult
The method

How a private AI and GPU infrastructure build runs.

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.

Engagement phases

Phases 01 to 04

  1. Name the constraint

    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.

  2. Price three options

    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.

  3. Build the platform

    GPU servers, networking, storage, model serving, identity, and monitoring. Standard engineering, plus a rollback path for when capacity assumptions turn out wrong.

  4. Prove the residency

    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.

Questions

On-premise AI questions, answered straight.

Answers first, including the ones that talk you out of buying hardware. An architect takes the call, not a salesperson.

Usually the Canadian region is enough. A residency requirement turns on where the data sits and who can compel access to it, and the major providers now offer Canadian regions with private endpoints and customer-managed keys. On-premise earns its cost when the data legally cannot leave your building, when a contract names the provider, or when your usage is heavy enough to beat rental economics. We price both and show you the working.

More than the hardware quote, and that gap is where budgets break. A GPU server is the smallest line on the page. Power, cooling, rack space, networking, storage, the model serving stack, the people who patch it at 2am, and the depreciation on silicon superseded inside two years all belong in the number. At low usage the rented version wins, sometimes by a lot.

No, and treating them as one thing gets expensive. Residency is where the bytes physically sit. Sovereignty is whose law reaches them, which depends on who owns and operates the infrastructure rather than just its postcode. Data in a Canadian region of a US-owned provider is resident in Canada and still reachable under US law. It's cheaper to ask that legal question before the build than after.

Yes, and it's the right answer less often than it gets requested. Air gapped means no network path out, which also means no vendor updates, no telemetry, and model refreshes done by hand on your own schedule. For classified or safety-critical work that trade is correct. For an internal knowledge tool it usually isn't, and the cost shows up in your team's time, not on an invoice.

When nobody can name the rule forcing it, because sovereignty as a preference gets costly fast. When the usage projection is a guess, since a half-idle rack is the most expensive way to own AI. And when the real problem is that nobody has decided who approves a model, in which case the fix is a decision, not a data centre. We'll say so before you sign.

Either of us, and you decide before we start. Some clients take it in house, so we hand over runbooks, patch schedules, and capacity baselines with the build. Others keep us on to patch the stack, watch how much of the GPU capacity is actually being used, and refresh models on a set schedule. The hardware, the tenancy, and the model weights are yours either way.
Keep exploring

Where this work connects.

These disciplines share the same senior team and tend to land together. Follow the thread to the next one.

Let's connect

Bring Us the IT Problem Your Last
Consulting Firm Handed Off

Explore our servicesRequest a consult

A senior SMEnode engineer replies within an hour, 24/7.