Every engagement begins with a question you can answer cheaply before committing to one you can't. Prototype, pilot, then scale on evidence. I work inside the stack you already have, so nothing gets ripped out.
I assess whether your knowledge can actually support the AI you're building on it, and tell you what it would take to fix. Most companies discover the problem is narrower and more fixable than they feared.
Why start here: it's scoped, it's fast, and at the end you know whether the bigger investment is worth making. If it isn't, I'll tell you.
Execution on the audit. We restructure your knowledge for machine retrieval and human learning at the same time, then install the lifecycle that keeps it current without anyone having to remember.
The outcome: one maintained source of truth that feeds your help center, your agents, your onboarding and your training, and stays right on its own.
Senior leadership for companies that need the function but aren't ready for the headcount. I've built this function from scratch four times, so you get the judgment without the salary.
I prototype fast and would rather show you something working than hand you a deck about it. If I've done the job well, the result feels like it was always there.
Learning is rarely a revenue line, so it's rarely funded like one. More than 20 years inside that constraint made me fast and lean. I won't propose a department you don't have.
Microsoft 365, Google Workspace, Slack, Zendesk, Confluence, Notion, whatever you've built on. I work inside it.
You get a perpetual license to every deliverable. I retain my own methods and tools, which is what lets me bring you everything I've learned elsewhere.
That's what the call is for. Tell me what's breaking and I'll tell you where to start, or whether I'm the wrong person.