Fifteen years of
production engineering.
Most AI consultants come from ML. I came from production systems. That's the difference.
I spent fifteen years building production systems at companies like Vimeo and Zumba — shipping features that moved business metrics, leading teams through platform migrations, and solving hard problems under real constraints. I was good at it. But I wanted more direct ownership of the work, autonomy in how it got built, and a closer line of service to the people I'm building for.
So I went out on my own.
The motivation
I've always had a passion for building things and being of service to people. Bringing engineering experience directly to small and medium businesses felt like the right way to do both. The biggest opportunities right now are in AI and automation, and most businesses don't have anyone technical enough to implement them well.
Production AI is a systems problem
Most AI failures aren't model failures. They're systems failures — bad context management, missing guardrails, no monitoring, no plan for how real people will trust, use, and abuse the thing.
Shipping past those failures takes the kind of systems thinking you get from fifteen years of production work: pattern libraries, failure-mode intuition, and the judgment that comes from putting real systems in front of real users. Years in the trenches is a feature, not a liability.
The work
I embed with teams to understand their domain, their problems, and their workflows — then build AI and automation systems that move real business KPIs. Increased revenue. Reduced costs. Faster operations. Doing this well requires that embedded element — you can't automate what you don't deeply understand.
I own and maintain everything I build as a long-term growth partner.
Who I work with
Engineering teams at mid-market B2B SaaS companies — Series B through D, roughly 50-500 engineers — who have shipped AI prototypes that haven't made it to production, or have AI on the roadmap and no clear path to ship.
Common signals:
- A working prototype that's been stuck in “almost ready” for 60+ days
- AI features on the roadmap, no in-house engineers experienced in shipping LLM-powered systems to production
- Engineering hours eaten by repetitive workflows that should be automated
- A demo that's impressive in front of users but won't survive real load, real costs, or real edge cases
I also take selective work outside this profile when the problem fits — e-commerce engineering teams, B2B services, internal automation. But the work I'm built for is helping engineering teams ship the Hard 20.