A global retail brand's workflows were handled entirely manually - creating bottlenecks, inconsistent follow through, and pulling staff away from revenue generating tasks.

Identified the bottleneck through direct operational observation, mapped the end-to-end workflow, and authored the solution spec. Managed the global rollout as UAT lead - from requirement sign-off through to staff adoption across all ANZ locations.

152 hours of productivity recovered per year - equivalent to $230,700 in value returned to front of house operations. Single-person dependency eliminated across all critical brand operations.

Data centres use water to cool the hardware that runs AI. There is published research on how much. There was no tool that connected those numbers to what you, specifically, were actually spending. The research itself had been built on assumptions from a different era of hardware entirely.

Built a tracker that runs offline: no API key, no network requests, nothing leaving the machine. Then, before publishing any numbers, emailed the paper's lead author to check the methodology. He wrote back. The original estimates, he said, shouldn't be applied to modern models.

The tool's own figures were revised 39 times downward and published that way, with the correspondence quoted in the methodology. A smaller number makes for a less alarming headline. It also happens to be correct. Every figure still ships as a range, because false precision is its own kind of lie. Read the build story →