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This example comes from the tool maker’s own case study, not an independent source.

A Two-Person Apparel Brand Makes Studio-Style Product and Model Images at Home With AI

Layer, a two-person sportswear brand, turns phone photos taken at home into studio, lifestyle and model images with Photoroom and sends them to Shopify. It says this saved hundreds of pounds on shoots and reports a 50% rise in online sales.

Who this is about: Two-person sportswear brand on Shopify (Layer, UK)

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What it takes

Cost
From $7.50/monthPro is $7.50 a month billed yearly, with a free trial.Price checked Oct 15, 2026 on photoroom.com. Prices change; check before you buy.
Setup time
Under an hourOur estimate from the source, for one business.
Skill needed
No codeApps and settings only.

Explain it simply

Good product photos usually mean paying a photographer, a studio and models. This brand lays a shirt on the floor, takes a phone photo, and an AI photo app turns it into a clean studio-style picture, a lifestyle scene or a picture of a model wearing it. They can fix small things like a logo or a colour by typing a few words, then send the finished pictures straight to their online store.

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What was done

Layer's website photos never matched its products. DIY shoots, hired photographers and a £500 full-day shoot for 25–30 products gave inconsistent lighting and colours and couldn't show details like linings and pockets.

Now each product is photographed at home on a phone (the first test was a T-shirt lying on the kitchen floor).

In Photoroom they generate studio-style shots, new backgrounds and lifestyle scenes, batch-edit whole sets, and use Virtual Model to show fit without a shoot.

Small details such as colours, logos and backgrounds are fixed with Edit with AI by typing keywords, instead of regenerating the image.

Finished images go straight to Shopify through the integration, and still shots become short motion ads for social with the Video Generator.

How it went

Layer reports a 50% increase in online sales, hundreds of pounds saved on shoots, faster launches and more social engagement. Its two-in-one running shorts, with an inner lining and phone pocket, took about five minutes to show properly, something earlier shoots couldn't do.

  • These results come from Photoroom's own customer story, with no time period or baseline given for the sales increase, and other changes may have contributed. AI-made model images should still reflect the real product's fit and colour.

Prompt

PromptWritten by our editors from the source
1. Shoot: phone photo of each product in even light on a plain surface.
2. Batch: run the set through background removal + one consistent AI background so every product page matches.
3. Lifestyle: generate 1-2 scenes per product that suit the brand (gym, running, street).
4. Model: use a virtual model image for fit; check fit, colour and details against the real item before publishing.
5. Fix: "Edit with AI" for small changes (colour, logo, background) instead of regenerating.
6. Publish: push to Shopify; make a short motion version for social ads.

Our outline of the workflow in the case study.

Business fit

Who this helps, and how:

  • Small apparel and accessories brands: get consistent catalog and lifestyle images for every product without booking shoots.

    Shopify & ecommerce
  • Shopify sellers: cut the upload step by sending edited images straight to the store.

    Shopify & ecommerce
  • Brands that post on social: reuse product shots as simple motion ads.

    Shopify & ecommerce

Related

Similar tools

Other tools for this job, and how they differ. Chosen by our editors, never paid for.

  • Pixelcut

    Close product-photo alternative with background removal, AI backgrounds, and batch export.

  • Claid

    Catalog-oriented AI product photography, including fashion-model style shots via API or web tools.

  • Canva

    Fine for lighter listing edits and backgrounds if you already design in Canva.

“Vendor or secondhand” (grade C) means: A tool maker's case study, a promotional demo, or someone else's write-up. How we grade. Spotted a mistake? Suggest a correction.