The short version
My career started in aerospace hangars building fail-safe systems for Airbus and Boeing. Learning to think clearly, ship carefully, and hunt for weak spots carried into every AI platform, compliance-heavy pivot, and workflow I have led since.
Away from the roadmap I'm a dad, Product School Toronto organizer, and a host to travelers from more than 40 countries because curiosity should stretch beyond work.
A dad to a 6-month-old. Shipping products and raising humans both depend on clear feedback loops, frequent iteration, and accepting that “good enough” beats chasing perfect.
I organize for Product School Toronto, mentor through TPMA, and contribute to the Atlassian community. Teaching forces clarity—if you can't explain your strategy to a room of PMs, you probably don't have one.
Morning coffee. Evening walks. Weekend projects that have nothing to do with work. These fuel creativity far more than most meetings.
Started in aerospace engineering, building systems for Airbus and Boeing where failure isn't an option.
Learned to think clearly, ship carefully, and obsess over what breaks.
Those habits still anchor how I build AI and SaaS products today.
MBA in Engineering Management because I realized I cared more about what we built than how we built it.
Moved to Canada in 2017 and traded hardware constraints for software speed, empathy, and iteration.
I've founded (FastDoc), pivoted (Protexxa), scaled (Tempo), and built something that never launched (Solution Tek).
Living all four taught me how to read signal, make bets, and keep teams aligned when pressure mounts.
"How do we make AI agents debuggable, not just capable?" The bottleneck in AI products isn't the model. It's trust. And trust requires transparency.
Intelligent workflows with n8n
Observability for AI agents, MCP servers for LLMs
Where the next wave of AI product value comes from (hint: it's not better models)