AI Consulting Services in Canada

Trusted by

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

Which of these is costing you right now?

Four of the reasons Canadian mid-market teams call us. If one of these is yours, the page below says which piece of work fixes it.

  • You've run six pilots and shipped none of them

    Every one of them demoed well and none survived contact with a real process. The gap is never the model, it's that nobody owned the integration.

  • The model's fine, your data is the problem

    Six systems, four definitions of a customer, and no lineage on any of it. No model fixes that, and every vendor demo quietly assumes you already have.

  • Your staff are already pasting client data into chatbots

    They're not being reckless, they're being productive with the tools they have. You don't have an AI policy problem yet, you have an AI policy vacuum.

  • It sounds confident and it's sometimes just wrong

    A generative system with no grounding and no evaluation will invent an answer rather than decline one. Fine in a demo, a liability in front of a customer.

The work

What AI consulting actually covers.

AI consulting services split three ways. Take one, take the set, or have us assess which one you're short on before you spend anything.

  1. Readiness and strategy

    Whether you're ready, and what to do first if you are. Data lineage, system inventory, a costed list of use cases, and an honest answer about which aren't worth building. This is the part of AI consulting a free proof of concept skips, and what stops six pilots becoming six write-offs.

  2. Build and automate

    Getting a working system into a process people already use. Retrieval over your own documents, agents with limits and logging, and the plumbing between the model and the systems of record. Most AI solutions providers stop at the API call, which is where the hard part starts.

  3. Governance and security

    Who approved it, what it's allowed to touch, and what happens when it's wrong. Policy, approval paths, logging, model red-teaming, and the evidence a client questionnaire asks for. AI advisory work is cheap at this stage and expensive after the incident, which is the whole argument.

By industry

Does AI Consulting Services work the same way in every industry?

No. The AI Consulting Services work is the same craft in every sector, but the conditions around it are not. In financial services that means OSFI's technology and cyber risk rules; in government, Protected A, B and C data handling; and 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.

  • OSFI's technology and cyber risk rules set what an examiner will ask to see.

    OSFI B-13. Technology and cyber risk across three domains: governance, technology operations and resilience, and cyber security. It binds federally regulated institutions, and only those.

    • OSFI B-10
    • OSFI E-23
    • Provincial credit union regimes
    Financial services IT
  • Protected A, B and C data handling decides where the work can run and who can touch it.

    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 goods rules, CMMC and the flow-down from your prime set what a supplier can do.

    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
  • Health information sits under provincial privacy law, and your IT provider is usually the custodian's agent.

    PHIPA. Ontario's health privacy act, with its own definition of a custodian and its own duties for the agents acting on a custodian's behalf. Your IT provider is usually an agent.

    • Provincial equivalents
    • PIPEDA
    • Breach notification
    Healthcare IT
  • Solicitor-client privilege decides how a firm can handle its data.

    Law Society obligations. Confidentiality and competence duties reach the technology you practise with, and the vendors who touch client information. The firm is expected to have assessed the provider, which means the provider should arrive with the answers.

    • Solicitor-client privilege
    • PIPEDA breach notification
    • Quebec Law 25
    Legal IT services

By the numbers

15+

Years of experience

40+

Enterprise deployments

100%

Senior engineers

24/7

Support

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.

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The method

How an AI consulting engagement runs.

Four stages. The first is where we say which use cases not to build, and some clients stop there.

Engagement phases

Phases 01 to 04

  1. Count the data, not the use cases

    We look at what data you hold, where it lives, and who's allowed to see it, before anybody picks a use case. Most AI consulting plans fail one layer below the model.

  2. Pick two, kill the rest

    We cost every candidate use case against the effort to build it, and recommend the two worth funding this year. Killing the other eight is the part that pays for the engagement.

  3. Build one thing properly

    One use case into production with grounding, evaluation, logging and a rollback, rather than five demos. A principal engineer does the build and stays reachable after it ships.

  4. Write down how it's governed

    Policy, approval path, logging and an owner, produced from the running system rather than drafted afterwards. This is what you hand an auditor, an insurer, or a client's security review.

The deep dive

Where Canadian AI consulting engagements actually break.

I run this practice, so I'll be direct about the two failures I see most, because neither is a model problem and both are expensive to unwind.

The pilot that could never have shipped

A team picks a use case that demos beautifully, builds it against a hand-cleaned extract, and shows it to the board. Everybody is impressed. Then production needs six system integrations, a data owner who never agreed to any of this, and a legal answer about where prompts go, so it sits in a folder marked phase two. The fix is unglamorous and it comes first: cost the integration and the data work before the model, because that's where the money and the calendar go.

Nobody has told you there is no Canadian AI act

Half the AI consulting pitches in this market imply a Canadian AI statute you have to comply with. There isn't one. AIDA died at prorogation in January 2025 and no successor has been tabled, so what actually binds you is privacy law, your sector regulator, and whatever your clients put in their contracts. That's harder than a single act, not easier, because the answer depends on your province and your customers. Anybody selling you compliance with a law that doesn't exist has not read the file.

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What do you need from AI Consulting Services?

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.

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Service
AI Consulting Services

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

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Questions

AI consulting in Canada, answered plainly.

The six questions we get asked before every engagement.

AI consulting should deliver a decision you can fund and a system you can run. Artificial intelligence consulting sounds abstract, so here's the concrete list: a data and systems readiness read, a costed shortlist of use cases with the weak ones named, one or two builds taken to production with grounding and evaluation, and a governance pack for a client or an auditor. If a proposal doesn't name the deliverable, it's a discovery phase wearing a project's clothes.

We won't print a number, and any AI consultant who prints one before seeing your data is guessing. What we can tell you is what drives it. The readiness and strategy work is the cheapest part, because it's bounded. Build cost is driven by how many systems the thing has to touch and how clean the data is, which is why we count the data first. Governance is the smallest line and the one people cut, which is how it gets expensive later.

Page one of that search is McKinsey, BCG and three directories, which tells you about the market, not what to do. The mid-market question is different: you want a team that can rack the GPU and write the retrieval pipeline, because enterprise AI consulting at the top end assumes you have an infrastructure partner already. Ask any AI consulting company you're talking to who builds the data layer. If the answer is a subcontractor, the integration risk is yours.

That's the normal starting point, and it's most of what AI consulting actually is. Nobody's data is ready, because it was collected to run a business rather than to ground a model. The question isn't whether it's clean, it's whether the use case you want needs the messy part. Often it doesn't, and a narrower scope gets you to production this year instead of a data programme with no end date in it. We'd rather tell you that than sell you the programme.

Either, and the choice is usually driven by your data rather than your preference. If you're handling health information under PHIPA, or Quebec personal data under Law 25, or you're a federally regulated financial institution, the on-premise or Canadian-resident option stops being a preference and starts being the requirement. We build both. We can because the same team does the data centre work, so GPU planning and model work aren't two vendors pointing at each other.

Most of them are reselling somebody's copilot licence, which is fine and isn't AI consulting. The test is whether they can answer two questions: where does the data physically sit while the model is reading it, and what happens when the output is wrong. If those come back vague, you've bought a licence with a logo on it. We run our own operations on the systems we build, which is the only reference that means much in a market this young.

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