I'm a strategic consultant, tech entrepreneur, and innovation leader — but more specifically, I'm currently running an AI company through its own commercialization, product, and go-to-market decisions, after already building a fifteen-year, five-industry business once before. If you want someone to tell you what innovation strategy should theoretically look like, there are firms for that. If you want someone who is, right now, the subject of his own advice, that's me.
No pitch deck, no pricing page — just a conversation about what you're actually trying to solve.
Every engagement starts the same way — a conversation about what's actually stuck, not a menu of packages. From there, the work tends to land in one of a few places, though it gets shaped around whoever's in the room, not forced into a template.
How an organization should actually think about adopting AI — not the version of this conversation shaped by a vendor with something to sell you, but the version shaped by someone currently building an AI company's own pipeline, partnerships, and go-to-market from the inside.
The mechanics of building something from nothing — commercialization, early product-market decisions, the unglamorous operating discipline that determines whether a venture survives its first few years. Drawn from Bedouin and from Kintrace, not a case study written by someone else.
Grounded in formal IP education (University of Toronto's IP Foundations, IPON's IP Strategy, Commercialization & Audit programs) and real commercialization work, for founders who need to understand what they actually own before they give any of it away.
Formats stay flexible on purpose — a single strategy session, a short advisory sprint with a defined deliverable, or an ongoing arrangement for a relationship that's already proven itself. The first conversation is where that gets decided, not this page.
This is for founders making a decision they can't fully see the shape of yet, and for organizations trying to figure out what AI adoption should actually mean for them before committing budget to it on faith. It's not a fit for someone looking for a packaged framework they can implement without anyone in the room who's actually done the thing — that's a different, and genuinely good, kind of product; it's just not this one. If that's closer to what you need, Work the Room is built for exactly that, and it's considerably cheaper.
This practice is new; the operating experience behind it isn't. As a graduate consulting engagement through the University of Waterloo, he served as Business Intelligence Consultant to GS1 Canada — developing business strategy recommendations for their global supply chain solutions, market research into traceability and sustainability trends, and customer acquisition strategy grounded in real data, not assumptions. In a separate engagement, he served as Research Analyst for Upside Robotics, running a full market and competitive analysis of the agricultural robotics sector — SWOT, PESTEL, and Value Net frameworks, primary research with actual farmers, and a strategic framework that directly shaped the company's product positioning.
He was invited back the following year to mentor the next cohort of students doing that same consulting work — the program that trained him in client-facing advisory now has him training others in it.
That, alongside the CMC-Canada membership above, is the credential; the growing list of paid client engagements is what comes next.
Bring whatever's actually stuck — a decision you can't quite see the shape of, a strategy you're not sure is right, a question about whether AI belongs in your roadmap at all. If it's a fit, we'll figure out the right shape for the engagement together. If it's not, you'll know that in twenty minutes instead of after a proposal.
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