Canada's AI ecosystem runs deep — a federal Pan-Canadian AI Strategy, three national AI institutes, a federally designated college research-hub network, and tier-1 research universities. G.E.T AI Labs is the applied-AI layer above that institutional foundation: where the research becomes a working system, deployed inside a high-stakes B2B environment.
In 2017, Canada announced the Pan-Canadian Artificial Intelligence Strategy — the first national AI strategy of any country. The federal government committed CAD $125M, administered through CIFAR, to establish three national AI institutes, fund the Canada CIFAR AI Chairs program, and build a coordinated research ecosystem across the country.
In 2022, the strategy was renewed as the Pan-Canadian AI Strategy 2.0 with an additional CAD $443M earmarked for commercialization, talent, computing infrastructure, and AI safety — including the creation of the Canadian AI Safety Institute. The result is a layered AI ecosystem with strong public-research foundations and an increasingly active commercial / applied-AI layer above it.
Canada's institutional AI layer is well-known by name to anyone in the field. Less well-known: the applied-research college tier below the national institutes, anchored by federally designated Technology Access Centres that work directly with operating companies.
Applied machine learning research, talent, and industry partnerships
Part of the Pan-Canadian AI Strategy. Affiliated with the University of Toronto and a national network of academic institutions.
Deep learning, reinforcement learning, and AI for science
Founded by Yoshua Bengio. One of the largest deep-learning research institutes in the world.
Reinforcement learning, machine intelligence, applied AI partnerships
Affiliated with the University of Alberta. Long history in reinforcement learning under Richard Sutton.
Federal program administration, AI Chairs, ecosystem coordination
Administers the Pan-Canadian AI Strategy on behalf of the federal government. Funds the Canada CIFAR AI Chairs program across all three national institutes.
Applied AI research with small- and medium-sized enterprises
Federally designated Technology Access Centre by Tech-Access Canada. Ranked #1 in Canada among medium-sized colleges for active AI research partnerships and completed applied AI projects.
AI safety research and model evaluation
Federal initiative within the Pan-Canadian AI Strategy 2.0. Coordinates with the US AI Safety Institute (USAISI) and the UK AI Safety Institute on international evaluation standards.
G.E.T AI Labs is not affiliated with Vector Institute, Mila, AMII, or CIFAR. Tejas Vyas (Principal) serves as Principal Investigator at the AI Hub at Durham College.
Above the institutional layer sits the commercial AI ecosystem — applied AI research labs, AI consulting firms, AI engineering studios, vertical AI product companies, and AI startups. This is where Canadian AI research crosses into client-facing engagement, shipped product, and deployed systems.
Each layer serves a different need. The categories below describe the major shapes of commercial AI work in Canada.
Commercial entities that combine research depth with engineering execution. Work spans domain study, technical landscape analysis, prototype development, systems evaluation, and deployment-ready AI products. G.E.T AI Labs sits here.
Strategy-led organizations that advise on AI adoption, technology selection, roadmap design, organizational readiness, and program governance. Output is typically strategic — written assessments, roadmaps, vendor selection guidance.
Build-led organizations focused on shipping production AI systems. Output is typically engineered — working LLM applications, RAG systems, AI agents, model fine-tuning, MLOps pipelines, and integrations.
Companies building AI-native products for specific industries — fintech, healthtech, legaltech, climate, biotech, defense. Their AI work is part of a broader product offering.
Early-stage companies, often venture-backed, building novel AI capabilities, foundation models, infrastructure tooling, or category-defining products. Canada has hundreds across the major AI corridors.
Both layers are useful. They serve different needs and operate on different timelines. This is the practical difference between engaging Canada's national AI institutes and engaging an applied AI company.
| Dimension | AI research institute | Applied AI company |
|---|---|---|
| Primary output | Published research, talent training, ecosystem programs | Client-owned systems, prototypes, evaluations, written technical artifacts |
| Engagement model | Membership, sponsorship, research partnership, talent recruitment | Engagement contract per scope or retainer |
| Timeline | Multi-year research and program cycles | Weeks to months per engagement |
| Funding model | Federal grants, member dues, sponsorship | Client fees or equity |
| IP ownership | Often institute-retained or jointly held | Typically client-owned at handoff |
| When to engage | Long-term research collaboration, talent pipeline, ecosystem positioning | Specific technical question, working prototype, evaluation, deployment |
G.E.T AI Labs is an applied AI research lab and consulting studio for high-stakes B2B work. The team operates from active positions inside the institutional layer of Canadian AI research — with affiliations at the AI Hub at Durham College, the University of Alberta, and senior engineering at HubSpot.
The work spans applied AI research, technical strategy, prototype development, and systems evaluation across 15 high-stakes industry domains. Engagements run from a two-week Technology Opportunity Mapping up to a 12-month Fractional CTO retainer, with a selective equity-based Founder Partnership program for technically ambitious early-stage companies.
G.E.T AI Labs is the applied-AI subsidiary of GodsEye Technologies.
The work is Canadian-anchored but the team is distributed and remote-first — we serve organizations beyond Canada in the United States, the United Kingdom, the Netherlands, and India.
Direct answers about the Pan-Canadian AI Strategy, the three national AI institutes, the federally designated college hub tier, and how applied AI companies relate to the institutional research layer.
The Pan-Canadian AI Strategy is Canada's federal AI policy and investment framework. Launched in 2017 as the world's first national AI strategy, with $125M of initial funding administered by CIFAR. It established three national AI institutes (Vector Institute in Toronto, AMII in Edmonton, Mila in Montreal) and a network of Canada CIFAR AI Chairs. In 2022 the federal government announced a Pan-Canadian AI Strategy 2.0 with $443M in additional funding focused on commercialization, talent, and the Canadian AI Safety Institute.
Three national institutes anchor Canadian AI research: Vector Institute (Toronto, focused on machine learning research, talent development, and industry partnerships), AMII — Alberta Machine Intelligence Institute (Edmonton, with deep roots in reinforcement learning), and Mila (Montreal, founded by Yoshua Bengio, a global leader in deep learning research). CIFAR is the federal-level program administrator that funds the AI Chairs across all three. Beyond the national institutes, Canada has a robust applied-research college tier — including the AI Hub at Durham College, which holds Tech-Access Canada's federal Technology Access Centre designation and is ranked #1 in Canada among medium-sized colleges for active AI research partnerships.
AI research institutes are not-for-profit or academic organizations whose primary outputs are research papers, talent training, ecosystem development, and public-strategy work. Applied AI companies are commercial entities whose primary outputs are working systems delivered to client organizations under engagement contracts. Institutes operate on multi-year research timelines and publication cycles. Applied AI companies operate on engagement timelines measured in weeks or months and deliver client-owned code, documentation, and deployed systems. Both layers are necessary in a healthy AI ecosystem; they serve different needs.
If you need original published research, long-term academic collaboration, talent pipelines, or membership in a research consortium — engage a research institute. If you need a working system, a technical prototype, an evaluation of an existing AI system, an AI adoption strategy, or production-ready engineering against your data and constraints — engage an applied AI company. Many organizations engage both: an institute for research collaboration and talent recruitment, an applied AI company for the build-out and deployment.
Canada's AI commercial activity is concentrated in three main corridors: the Greater Toronto Area (the largest cluster, anchored by the Toronto-Waterloo tech corridor), Greater Montréal (anchored by Mila and a strong deep-learning research base), and the Edmonton–Calgary corridor (anchored by AMII and the University of Alberta). Significant additional activity exists in Vancouver, Ottawa, Halifax, and increasingly through distributed and remote-first applied AI companies operating across multiple provinces.
The Canadian AI Safety Institute (CAISI) was announced in 2024 as part of the Pan-Canadian AI Strategy 2.0. It is a federal initiative focused on AI safety research, model evaluation methodology, and public-interest AI risk work. CAISI operates in coordination with similar national AI safety institutes in the United States and United Kingdom, contributing to international AI evaluation standards.
Yes. Canadian applied AI companies regularly work across regulated environments — federal and provincial government, healthcare under provincial privacy regimes, financial services under OSFI prudential expectations and the federal Bank Act, critical infrastructure under TSA/transportation regulations, and defense-adjacent work under appropriate clearance regimes. Choice of partner depends on the specific regulatory regime and the applied AI company's prior experience inside it.
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