
Diagnose ▸ Design ▸ Scale
We help growth-stage software companies navigate their next stage — from sharpening strategy and go-to-market to deciding where AI belongs and preparing for partnership, investment, and acquisition."
We work as operators, not spectators. The guidance comes from having done the work — building emerging technology companies through every stage, from independence through acquisition and scale, on the inside.
That means strategy built to be executed, not just presented, go-to-market grounded in what actually moves revenue, and market and transaction decisions informed by having lived them. Every engagement is led directly by that experience.
We are principal-led and built for focused, senior work. Every engagement is led directly by senior expertise, with access to a broader network of trusted operators, advisors, and specialists when additional depth is needed. This model keeps the work sharp, flexible, and grounded in real operating experience.

We sharpen how the business grows — strategy, positioning, and the go-to-market motion that turns product into revenue. For companies scaling past early traction, launching new products, or selling an acquired portfolio through a GTM that wasn't built for it.

We help you decide where AI belongs in the business and where it does not: the ambition, what the organization can realistically absorb, and the posture for each part of the operation. For companies being pulled in every direction by vendors, pilots, and internal requests, who need a strategy that holds under that pressure instead of being set by it.

Preparing a company for its next transaction — acquisition, partnership, or investment — is its own discipline. We help you get the business ready: the story, the operating proof points, and the readiness that lets you engage acquirers, partners, and investors from a position of strength.
Patterns in the Long Arc
Essays on what the AI transition reveals about the companies moving through it. The recurring finding is that most of what looks like an AI problem is an older one the technology has finally made visible: how decisions actually get made, where value is genuinely produced, what the economics have been hiding, and what happens to accountability when work stops being done the way it always was.