AI in production

What it takes for LLM applications to work beyond the demo: cost, quality, operations — from my own platforms.

2026-10-27 · AI in production

Cutting out vehicle photos with AI: the model was the smaller part

A vehicle dealer has the images from its 360° studio processed by an external service. The question was whether we could do it ourselves at least as well. Segmentation was solved quickly — the difficulty lay elsewhere.

2026-10-25 · AI in production

The model reads, the code decides

Three bugs in three days across two AI systems — and the same answer each time: the language model is for language and for extracting facts. Whatever can be decided deterministically is decided by code.

2026-10-23 · AI in production

A golden set for the AI assistant: tests that check actions, not sentences

One day of manual testing, nine different model failures. Every fix could break the neighbouring case, and only manual testing could tell. So I built regression tests.

2026-10-20 · AI in production

Two AI reviewers and a full data run: how an agent gets safely to production

In one day an AI agent designed, wrote and shipped a change across two repositories. The question was not whether the model can write code, but how its mistakes get caught before production.

2026-10-20 · AI in production

When a feature store pays off — criteria from running a GenAI platform

LLMOps checklists list the feature store as a standard component. For a multi-tenant GenAI platform I checked which problem it solves — and how to tell whether you have it.

2026-10-17 · AI in production

Ten GPU hours a month: why we did not build a GPU cluster

Our own AI pipeline for vehicle photos worked as a prototype. Next question: on which hardware, if the data must stay in the EU and the rest of the application runs on Cloudflare.

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