An internal AI tool for an ops team that didn't want one

Built a small internal tool last month for a manufacturing client in Mississauga. The team that used it didn't ask for it and was, on the first call, openly skeptical. Six weeks later they're the ones flagging when it goes down.

The job: their ops lead was spending the first ninety minutes of every morning pulling numbers from three systems — an ERP, a shipping platform, and a shared spreadsheet — into a single email summary for the floor managers. Standard internal AI tools / workflow automation territory. Nothing exotic.

What I built was deliberately small. A scheduled job that pulls from all three sources at 6 a.m., runs the numbers through a model with a tight prompt that knows the client's specific lexicon (their product codes, their site names, the difference between a "short" and a "miss" in their language), and drafts the morning summary in the ops lead's voice. He reviews it for ten minutes, fixes anything off, and sends it.

Two things I had to fight for during the build:

The model is not allowed to invent product codes it doesn't see in the source data. This sounds obvious. It is not, in practice, the default behavior. About 40% of the prompt is anti-fabrication rules, which is the same shape of problem I wrote about with the audit tool.

The tool does not auto-send. It drafts. The ops lead was the one who insisted on this and he was right. The trust gap closes faster when a human is still the last set of eyes for the first month or two. After that, you can talk about automation. Not before.

What surprised me was the second-order effect. The ops lead now has ninety extra minutes a morning, which he's been spending on the floor instead of at his desk. Three weeks in, the floor managers said he'd caught two recurring issues nobody had time to look at before. The tool didn't catch those. He did. The tool just gave him back the morning.

Most of the work I do ends up shaped like this. Not a flagship AI product. A piece of plumbing that deletes a meeting or saves a morning, designed by someone who's spent a long time watching how people actually use software.