Most teams pick AI infrastructure based on price and performance. Then legal gets involved, and the conversation changes fast. That's because data residency AI infrastructure decisions aren't just technical — they're legal ones. Where your training data sits, where your model runs, and where your

Building multi-agent AI platforms? Discover the 4 core architectural patterns -from Orchestrators to Critic-Actor loops - and learn how leveraging reserved GPU instances can slash scaling costs by up to 20x while permanently eliminating real-time inference bottlenecks. Architectural Patterns - Compute Infrastructure Strategies The Core Problem & Solution * The Challenge:

Most explanations of an ML workflow stop at the model card. That's the easy part. The real work — turning raw files into usable data, training a model on actual hardware, checking it's good enough, deploying it somewhere your product can call, and watching what happens once

If you're building an AI product in Canada, you've probably hit this problem: your inference API lives in the US, your GPU rental is on a US hyperscaler, and your storage bucket sits wherever egress pricing was cheapest that month. Each piece works fine on its

Privacy Impact Assessments were designed in a world where data risk lived in databases. You mapped what personal data you collected, where it was stored, who could access it, and how long you kept it. Simple enough — if your tech stack was simple. AI workloads broke that model. And the

Retrieval-Augmented Generation (RAG) is one of the most practical AI patterns available right now. Instead of relying solely on what a model learned during training, RAG lets you attach your own documents, databases, and knowledge bases to the model — giving it accurate, current, proprietary information to work with. The catch:

There's a problem in AI infrastructure that doesn't get talked about enough: power. Everyone's racing to get their hands on NVIDIA's new Blackwell Ultra GPU, the B300, and for good reason — it's a serious leap forward for FP4 inference and

No computer science degree required — just a clear explanation of one of the most important concepts in AI task orchestration If you've spent any time reading about AI agents, workflow automation, or orchestration platforms, you've probably encountered the term "dependency graph." It gets used

Building an AI prototype is an exhilarating experience. You start with a great idea, a few lines of Python, and a public API key. It works on your machine, it wows your stakeholders, and it feels like you're only a few steps away from a finished product. But

We’ve officially hit peak hype. "Agentic AI" is the latest tech buzzword being slapped onto just about anything with an LLM attached to it. Got a customer support chatbot with a slightly longer system prompt? Agentic. A Python script that calls an API twice? Agentic. A basic

Every tech company on the planet is currently trying to sell you something "agentic." But if you strip away the flashy marketing decks and the venture capital hype, what are we actually talking about? Let’s skip the jargon and answer the most frequently asked questions about Agentic

The Canadian AI landscape in 2026 is defined by a dual pressure: maximizing computational efficiency for large language models while strictly adhering to domestic data sovereignty laws. With the widespread availability of NVIDIA's Blackwell architecture, enterprise tech leaders face a critical architectural decision, one that carries both performance

Last week, Prime Minister Mark Carney stood in Toronto and announced something that should be on every Canadian founder's radar: a national AI strategy with a target to take Canada's AI adoption rate from just over 12% today to 60% by 2034. Read that again. 12%