How the tools work together

AI Fashion Suite is not one feature. Four of its tools sit in one app because each one makes the next one better, and this page explains what each does on its own and then the part that matters more: how they hand information to each other. If you read one page before setting up, read this one.

The four that feed each other

Virtual Try-On puts the garment on a photo of the shopper. They tap “See it on me”, add a photo, and get a picture of themselves wearing the item. It answers “what does this look like on a body like mine?”

Find My Size asks a few short questions - height, weight, how they like things to fit - and recommends a size against your size chart. It answers “which size do I order?”, the question that otherwise turns into a return.

Reviews collects ratings and photos from real buyers, with one extra question a generic review app cannot ask: how did the size run - too small, true to size, or too large. That single answer ties the whole suite together.

Size Learning is what happens once those fit answers add up. A returning shopper sees the size they had last time and how they rated it, and the size recommendation shifts for everyone based on what buyers actually reported.

How they feed each other

Here is the loop, in order. A shopper tries the item on, checks their size, and buys. After delivery they leave a review and mention it ran small. Do that across a handful of buyers and a pattern forms: most people say this style runs small.

Find My Size reads that pattern. When the next shopper asks for their size, the recommendation already knows the style runs small, nudges them up a size, and says why - in plain words, in the popup. Fewer people order the size that was going to come back.

What to set up first

1. Add the theme blocks to your product template in the theme editor - the See it on me button, Find My Size, and the Reviews block.
2. Load one size chart so Find My Size has measurements to recommend against.
3. Leave the fit question in Reviews on (it is on by default) so the size-learning loop starts collecting from day one.
4. Give it a few weeks. The size recommendation sharpens on its own as reviews arrive - nothing else to do for the loop to run.

And two that stand apart

Back in Stock and Wishlist are not part of that loop. They answer a different question: not “will this fit me?” but “what happens to the shopper who wanted it and did not buy it today?”

Back in Stock catches the one who found the size gone. They leave an email against that exact size, hear the moment it returns, and the list of who is waiting becomes a demand report for your next order. Its low stock counter catches the shopper one step earlier - the one who is about to be too late.

Wishlist catches the one who liked it but was not ready. They save it, guests included, and what gets saved is demand that has not converted yet - which is the signal you want before deciding what to restock, feature or discount, rather than after.

Both are bought separately because neither needs the other tools and the other tools need nothing from them. Between them they cover the two ways a ready shopper leaves without buying: the thing was gone, or the moment was wrong.

Each tool has its own section in these docs with every setting. Start with the one that solves your biggest problem - if size-related returns hurt, that is Find My Size and Reviews working together. If you sell out of your best sizes, start with Back in Stock.