Every company is adding AI to their go-to-market. But the ones seeing results built the infrastructure first — clean data, structured process, accountable teams.
I build the systems that make your AI investments actually work.
"The companies winning with AI didn't start with the tools. They started with the operating system."
Most B2B companies are three steps into an AI transformation and getting inconsistent results. Not because the tools are wrong — because the data and revenue system underneath them was never built to support it.
The tools surface the problem. Someone still has to fix it. That work has to happen before — or alongside — any AI investment if you want it to actually perform.
The companies that aren't hitting their growth targets almost always have a pipeline they can't trust, customers they're losing before they should, and expansion revenue they're leaving on the table. The Intelligent Revenue Engine addresses all three — built on a foundation of Structure and Governed Data that AI can actually run on.
AI amplifies what’s already there. Build the foundation first.
See how it works →You've achieved undeniable product-market fit and customer love, but product velocity has outpaced your commercial infrastructure. The board expects predictable, explosive growth, but your GTM playbook is still tribal. You need to build a professionalized, AI-native revenue operation from scratch — the right way, the first time.
Revenue is healthy, but you are still personally pulling the levers on most major deals. You are officially the operational bottleneck. To unlock your next phase of growth, you need to transition from founder-led sales to a highly structured, repeatable system that runs cleanly without you in the room — and that an AI stack can actually optimize.
You have an aggressive value creation mandate and a strict investment thesis timeline, but you inherited a fragmented legacy revenue org. You need an operator who can diagnose pipeline leakage fast, implement rigorous execution cadences, and hand off a high-predictability system that holds long after they leave.
A structured assessment of your full revenue system — strategy, process, pipeline, team, and technology.
Once we have a clear picture of your revenue system, we prioritize the highest-impact opportunities first.
Every company is different. Some need an embedded fractional CRO working inside the team. Others need focused advisory on a specific workstream. We define the right engagement model based on what your business actually needs.
Every playbook, process, hiring framework, and AI investment we recommend is built to last. The goal is a proven revenue system that leverages AI and delivers real business outcomes.
A real pipeline numberGate-enforced pipeline with documented qualification criteria. No more guessing your forecast.
A team structure that scalesRight roles, right profiles, right comp plans. Hiring scorecards and onboarding that cuts ramp time in half.
Stage advancement that means somethingDeals move on verified buyer activity — not rep optimism. Every stage reflects reality.
An expansion motion that generates NRRCS and expansion separated into distinct functions with clear accountability and AI-monitored signals.
I spent 15 years at Domo helping build the sales organization from the ground up — from first customer to $300M ARR and through a public market listing. Before that, I was at Omniture selling data and analytics to CMOs when most companies still thought Excel was good enough. I've competed against Microsoft and Salesforce at every stage of company growth and I know what it takes to win as the upstart.
What I've learned across all of it: most revenue problems aren't people problems or tool problems. They're structure problems. Bad pipeline hygiene, missing qualification gates, unclear stage advancement criteria, a CS org trying to own both outcomes and expansion. These are the things that kill forecast accuracy and slow revenue growth — and they're fixable with the right operating model.
I built the Intelligent Revenue Engine to give companies a framework for exactly that — a system organized around three outcomes: Grow new revenue, Protect what you've already built, and Expand inside the accounts you have. It maps what AI should own, what experienced humans must own, and how to connect the two into a revenue motion that scales without adding headcount proportionally.
I work with a small number of companies at a time. I'm inside the business — in the pipeline reviews, on the calls, building with the team. Not advising from a distance and sending a slide deck.
I'll ask you four questions about how your company makes money, manages cost, retains customers, and deploys people. You'll know within 30 minutes whether there's a problem worth solving together.