Short write‑ups of problems we hit running a real commerce operation and what we changed in the code because of them. No product announcements; just the reasoning.
The next thing we are building — an MCP server for customers, so a trade buyer or a dropshipper can connect their own AI assistant to their account and ask, browse and eventually order without an integration project. What it will do first, what it will not do until someone accountable says so, and the groundwork already in place.
We did not embed the business into a vector store and hope the right chunk came back. We gave the model forty‑five tools — the same actions the staff app runs, under the same permissions — and a guide on how to use them. The result is a report that used to take three people a day, or a ticket to engineering, arriving in thirty seconds with real figures. The reactions, in order: disbelief, checking whether the numbers were random, and then a very quiet "oh".
We log every call an assistant makes to our MCP server — who, which tool, how long, did it fail and why — and put it on a sysadmin dashboard. Two weeks of data found a user whose assistant had made 454 calls with a 100% error rate, and fixed the server's instructions instead of the user.
In 2022 we picked Laravel because it was the easiest way to find programmers for a system that would take years to build. In 2026 we would pick it again, for a reason that did not exist then — the models know it better than any hire ever would. What the choice bought us, what it cost, and what "is Laravel still worth it" actually asks.
We replaced a third-party storefront search with our own — Typesense, typo tuning measured against real queries, synonyms shared across fourteen languages, merchandising boosts for staff, a hybrid semantic arm tuned with a harness instead of a hunch — and then turned the queries that found nothing into a shopping list for the buyers. Total AI spend for the whole programme: about six cents.