AI CASES
Every case follows the same shape: the problem, the approach, the result, and what went wrong along the way. That last block is not decoration. A project where nothing ever broke tells you nothing about whether someone actually built it.
An organisation does not adopt AI by buying tooling but by changing the way it works. I build the layer around that: working sessions, a knowledge base that stays current, and a recurring update showing what has become possible.
The admin around a gym is largely repetition: assembling programming, keeping track of what has already been done, and recording it in several places. That now runs as one system that composes a full week and submits it for approval.
The site you are reading is the case itself. Design and code come out of Claude Code and Lovable, the logo was generated rather than drawn, and work only reaches production after review. Not a presentation about the approach, but the approach itself.
The admin around A/B testing was entirely manual: title, hypothesis, description, and then tracking the status yourself. It became a form with a workflow underneath, and within a year the number of tests tripled. This was my own work that I automated, which explains why adoption was the easy part.