The Thread

ChatGPT creates a new image; Botika reproduces a garment you already stock. Where each one is the better choice, and the test that settles it on your own product.

A job-by-job matrix for fashion imagery: which tasks need an image invented, which need a specific product reproduced, and the right class of tool for each.

What ChatGPT, Claude, Midjourney and Higgsfield are each actually built to do, why general image models reinterpret a garment rather than reproduce it, and where each one fits in a fashion workflow.

Seven things to test before buying an AI product imagery tool, why the vendor demo is the least useful evidence, and how to score output on your own hardest products.

The four methods brands use to photograph clothing, what each is genuinely good at, and how assortment width and release frequency decide which one your catalogue needs.

What a fashion product image generator actually produces, the specification your output has to clear before it can go on a product page, and how to test one on your own hardest garment.

How to produce imagery across a full ecommerce catalogue to one consistent standard, why volume breaks a shoot-based workflow, and how to run a catalogue-scale batch.

Whether you can put clothes on an AI model for free, what free tools handle well, and the four requirements that change once an image goes on a product page.

A breakdown of what AI fashion photography costs against a traditional photoshoot, why cost per usable image is the metric that matters, and how to price it for your own catalogue.