We kept hitting the same wall
The four of us spent years in large enterprises — building teams, running transformations, shipping products. The problem was always the same: nobody could easily answer how the organisation actually worked. Who could decide what. Who owned which services. How work was supposed to flow.
That knowledge lived in documents that were out of date the moment they were published. In org charts that showed hierarchy but not authority. In the heads of people who'd been around long enough to know — and who'd eventually leave, taking everything with them.
Sixty conversations later
We spent a year talking to over 60 enterprise leaders — COOs, CIOs, transformation leads, platform engineers. The pattern was universal. Organisations were spending €500K to €5M per year on consulting firms to document their operating models. The output was a PowerPoint that was obsolete within 12–18 months. And nobody was calling it an infrastructure problem.
They were calling it a documentation problem. A change management problem. A people problem. But every time, the root cause was the same: no machine-readable, always-current source of truth for how the organisation actually worked.
Then AI agents arrived — and the gap became urgent
AI agents changed the stakes entirely. Enterprises started deploying copilots and autonomous agents — and immediately hit the same wall. Agents couldn't route decisions correctly because there was no machine-readable source of truth for who had authority. They couldn't escalate appropriately because approval paths weren't queryable. The absence of operating model infrastructure went from being a productivity problem to a strategic blocker.
That's when it became obvious: this wasn't just a better org chart. It was foundational infrastructure for the AI era. And it needed to be built as infrastructure — not as documentation that becomes stale, but as a living system that other tools depend on and therefore keep updated.