The question was simple.
The answer wasn't.

Why we exist

What was our churn last month? Lost logos, says Sales. Lower MRR, says Finance. MAU decline, says Product. And what do you mean "month"? Calendar? Last 30 days? What sounded like a simple question becomes a negotiation, a chain of follow-up emails, Slack threads, and reconciliation meetings. Days later, sometimes weeks, you get an answer nobody fully trusts.

And this is just one metric. The chief of staff at the CFO office of one of our customers used to spend more than 30% of his time on data reconciliation. He was good at it because he knew who to ask. He was the system. The human API bridging SAP, Salesforce, Workday, Netsuite, and a dozen other sources, each with its own definition of every metric that matters.

Now imagine he's gone, and AI is next in the queue.

For decades, enterprise software captured records. What it never captured was meaning. Every system learned its own language. Every department developed its own dialect. As long as humans were in the loop, people who could translate, reconcile, and make sense across systems, the work got done. Slowly, expensively, but done.

Then came AI, which is powerful in isolation but inherits every ambiguity underneath it and amplifies them. An agent querying five systems with five definitions of revenue produces confident noise, and it produces it faster than any team could. That is why so many AI rollouts stall. Behind each stalled rollout is someone like that chief of staff, spending years of their working life translating between systems instead of doing the job they were hired for, on work a machine should have handled.

Ohno built the Toyota Production System to eliminate the waste hiding inside every manufacturing process. We're doing the same for enterprise knowledge: removing the reconciliation work that turns smart people into human APIs between systems and keeps AI from delivering on its promise.