I turn growth priorities into operating systems.
Forecasts, reporting cadences, AI workflows, and 0→1 systems — the connective tissue between strategy and execution. My résumé shows the roles; this site shows how I operate. Scroll to descend.
Proof, at a glance.
I don’t trust a number I haven’t taken apart.
In commercial operations the top-line view is usually too blunt to be useful. Demand behaves differently by account, supplier line, reorder cycle, and margin profile — so I treat a forecast as a system to decompose, not one number to defend. Separate the durable from the temporary and the noise, and attention goes where it can actually change the outcome.
Strategy only matters when it becomes a cadence.
Through a post-acquisition integration, I worked as the strategy-and-operations partner to the President. The work was translation as much as analysis — leadership priorities, sales realities, finance constraints, and parent-company expectations resolving into one rhythm of forecasts, budget tracking, and commercial follow-through. A plan is only useful when other people can run it, repeatedly.
Push the money into the product — automate the rest.
I use AI to remove recurring work that shouldn’t need a person twice — newsletter production, research synthesis, multi-model review, reporting drafts. In my last venture that meant an operating stack where software absorbed the fixed costs, so a lean team could do more before headcount caught up. Not AI as a badge — AI as workflow infrastructure.
I build from ambiguity.
I’m happiest where a business needs structure before it needs headcount. In a pre-launch DTC venture I owned the business function end to end — product strategy, launch planning, financial modeling, and the operating stack behind it: Shopify, Klaviyo, Power BI, and LLM workflows fitted into one spine a small team could run intelligently, before scale justified more complexity.
The discipline I trust most is knowing when to pivot.
A good operator doesn’t just build momentum — they know when not to fund it. I spent two years architecting a machine-learning trading system and wound it down when it proved technically sound but not commercially compelling. I made the same call on a venture when changing economics broke the case before launch. I trust intensity; I trust commercial discipline more.
Founder-level ownership, operator by craft.
I’m most useful where growth creates operational complexity faster than the organization can absorb it. I grew up inside two family businesses and have been building ever since — now I’m bringing that operating instinct, AI-native by practice, to a high-velocity, scaled team, and committing to it for the long run.