The engine I built for my own product
Storytime, my app for toddlers, grows through creators, not ads. I needed small parenting channels whose audience genuinely fit. I was finding maybe ten a week by hand. Search, open the channel, scroll the videos, dig out the email, write a note, repeat. Two hours a day, and I still wasn't keeping up.
So I built the engine. It runs on its own, no morning queue for me to approve. It pulls hundreds of channels, scores each one against the rule I built for whether their audience is really mine, throws out the rest, finds the owner and the email, and sends the offer. Then it hands me the people who write back.
193 contacted. 74 wrote back. That's 38 percent.
Here's the decision every tool throws away, the one I told you I'd come back to. It wasn't the scraping and it wasn't a clever subject line. It was the offer itself: I framed it so the creator won, plainly, before I ever won. Their audience got something real, they got paid, I got an honest review. That framing, plus only ever contacting people it actually fit, is the whole 38 percent. That's judgment, written into the machine. It's the part a template can't give you.
That's judgment, written into the machine.
It worked well enough that people kept asking me to build them one. So now I do.
The same shape, built for a B2B sales team
I built and live-tested the same engine for a recruiting firm that places engineers in manufacturing. Different fuel, same shape. Instead of YouTube channels it sources open roles and companies, researches each one, scores it against exactly who they want to work with, and drafts a tailored angle their team can run. It's a real, working build, tested on live data, not a slide. It's proof the engine that grew my own product carries straight into a B2B sales motion, and that the judgment layer transfers with it.
Before any of this
For twenty years I removed work inside companies, encoding how people decided into systems they could actually run. One of those, before LLMs existed, untangled a corporate approval maze that used to drag on for months: red tape, meetings, approvals that looped back on themselves. We cut that down hard, made sure the right approvers saw the right projects, gave everyone visibility into where things stood, and killed a pile of errors along the way. On a conservative estimate it gave back seven figures a year in time across roughly 500 people. I also built an AI coaching platform in early 2023, before custom GPTs existed, that's now featured in Forbes, Inc., and the BBC.
The work has been the same the whole time: understand how someone good at their job actually does it, build that judgment into something that runs on its own, and give them back the part they wanted to be doing.