Tomas David Ye

We stopped celebrating recommendation accuracy. We started measuring overrides. That changed almost every conversation we had with warehouse operators. A model can recommend the right carton 99% of the time and still fail if people don't trust it enough to use it. An override isn't always resistance. Sometimes it's a broken process. Sometimes it's outdated warehouse logic. Sometimes the operator knows something the system hasn't learned yet. That's why I don't think override rate is a failure metric. It's a learning metric. Every override is evidence that the software and the operation disagree about reality. The job isn't to eliminate every override. The job is to understand which ones should disappear and which ones should teach the system something new. The fastest way to lose adoption is to treat every manual decision as user error. What has your team learned from the decisions people refused to let the software make?

We built an AI onboarding assistant. Answer any question, day one, no waiting on a trainer. Turnover in the first two weeks didn't move at all. We sat in on floor inductions to find out why. New hires weren't asking the assistant questions. They were asking Marisol, a picker three years in, because she'd answer with "yeah, and also" and tell them the thing that wasn't in the manual. Which bathroom actually has paper towels. Which supervisor to avoid asking twice. That the AC in bay 4 only works if you don't mention it's broken. The assistant answered questions correctly. Marisol answered questions and told them they were going to be okay here. We stopped trying to replace her and started paying her for two extra hours a week to do onboarding walk-throughs, with the AI handling only the paperwork behind her. Two-week turnover dropped 30% in the next quarter. We'd built a very good answer machine for a trust problem.

People ask why we spend so much time inside customers' warehouses instead of just shipping the software and moving on. honestly it's because that's where we find out what we got wrong. you write the roadmap in a conference room and it sounds airtight. then you're standing there during a shift change, watching someone try to use what you built, and you realize you never accounted for the fact that shift change is chaos, everyone's rushing, nobody's reading the screen carefully. You don't get that from a feature request email. you get it from standing next to the person actually using the thing at 6am when they're tired and just want to get through the shift. We started calling ourselves "forward deployed" somewhat jokingly at first, but it stuck because it's true we don't think go-live is the finish line, it's honestly where the real learning starts. I'm not trying to prove the software was right when we built it. most of the time it wasn't, not entirely. i just want to leave every deployment knowing more about how the warehouse actually runs than i did walking in. curious what's something you only figured out after actually being where the work happens, not before

For the past few years, we've been building quietly. Visiting warehouses. Talking with operators. Sitting down with founders. Learning from every deployment, every customer, and every mistake. Most of those conversations never made it online. We're changing that. From today, you'll see a lot more of what happens behind the scenes: warehouse tours, customer visits, industry events, product decisions, and the realities of building AI for logistics. We're starting at Fetch Fulfillment, one of our earliest customers. In this first video, we go inside a real 3PL to show how a warehouse actually operates, how orders move from click to shipment, and why the small operational decisions most people never see are often the ones that matter most. This is the first of many. Watch the full video on YouTube. Link is in the comments. We'd love to hear what you think.

We crashed our own demo in front of a prospect because one SKU in their test data had negative dimensions. Not missing data negative. A box logged at roughly -4 x 12 x 8 inches, sitting in their warehouse management system for years, quietly excluded from every report that should have caught it. Our system choked on it live, on a call, in front of the person deciding whether to sign. We didn't talk our way out of it. We showed them the SKU, showed them how it got there, and told them their existing tools would never have caught it because nothing in their stack was built to expect an impossible number. They signed anyway for exactly that reason. The bug we were most embarrassed by became the strongest part of the pitch.