A Day on the Storefront
Most department stores teams know the pattern: plenty of browsing, plenty of returns, and a fuzzy picture in between. In Department Stores, the product page has to do what a fitting room once did — and flat photos rarely manage it. The way a department stores brand lets people picture a garment on themselves says a lot about how it converts.
The Challenge
For a Director of Digital, no way to capture body and preference signals tastefully is more than an annoyance — it is a steady drag on conversion and margin. It rarely starts as a crisis; no way to capture body and preference signals tastefully builds quietly until a returns report or a soft launch makes it impossible to ignore. A recurring challenge for department stores is no way to capture body and preference signals tastefully. Left unaddressed, no way to capture body and preference signals tastefully compounds: confidence drops, returns rise, and the catalog feels flat.
What Mirari Does
Mirari pairs a believable try-on with a design studio, so the same tool that shoppers try on in is the one your team designs in. Rather than another flat gallery, Mirari puts the garment on the shopper’s own body, live, with their real arms and hair in front of the cloth. Mirari tackles this with Semantic product search: Natural-language and visual intent are matched to the right garment, with a keyword fallback so search always works. Since semantic product search sits within the AI Perception part of Mirari, it fits naturally into how department stores teams already work. This is where Mirari comes in — the AI-powered virtual try-on and garment-design app built by ZadeNor AI.
Under the Hood
A segmentation pass lets their real arms, hands and hair render in front of the garment, so it reads like a mirror, not a sticker. Prefer an avatar? A rigged 3D model mirrors pose, face and hands, with flowy spring-bone cloth motion. Getting started is straightforward: drop in a glTF garment or generate one from a template, and it is ready to try on.
The Win
For department stores, that means less sampling and photoshoot spend the whole team can rely on. The result is less sampling and photoshoot spend, without a render farm or a per-session GPU bill. Shoppers get a believable look at the fit; the brand gets fewer returns and more confident checkouts. Try-on stops being a gimmick and starts being a default on every product page.
See It in Action
From a design idea to a try-on-ready SKU, Mirari by ZadeNor AI keeps Department Stores merchandising fast and your shoppers confident. Let customers see the look on themselves before they buy.
For leaders, the real risk is strategic: a try-on gap becomes a ceiling on how far the brand can scale online. What looks like a product-page problem is often a fit, confidence and returns problem in disguise. Brands using this approach see Less sampling and photoshoot spend across boutiques and online. The result is less sampling and photoshoot spend, without a render farm or a per-session GPU bill. Shoppers get a believable look at the fit; the brand gets fewer returns and more confident checkouts.
Every shopper who cannot picture the fit is a basket left half-built. Teams end up reshooting and discounting instead of merchandising with confidence. Shoppers get a believable look at the fit; the brand gets fewer returns and more confident checkouts. For department stores, that means less sampling and photoshoot spend the whole team can rely on.
The cost of no way to capture body and preference signals tastefully is rarely a single number — it is lost conversion, return shipping, and a catalog that underperforms. Teams end up reshooting and discounting instead of merchandising with confidence. Brands using this approach see Less sampling and photoshoot spend across boutiques and online. The numbers follow the confidence: higher add-to-cart, fewer bracketed orders, and drops that land.
Teams end up reshooting and discounting instead of merchandising with confidence. For leaders, the real risk is strategic: a try-on gap becomes a ceiling on how far the brand can scale online. Brands using this approach see Less sampling and photoshoot spend across boutiques and online. The result is less sampling and photoshoot spend, without a render farm or a per-session GPU bill. For department stores, that means less sampling and photoshoot spend the whole team can rely on.
What looks like a product-page problem is often a fit, confidence and returns problem in disguise. Teams end up reshooting and discounting instead of merchandising with confidence. Brands using this approach see Less sampling and photoshoot spend across boutiques and online. Shoppers get a believable look at the fit; the brand gets fewer returns and more confident checkouts. Try-on stops being a gimmick and starts being a default on every product page.
For leaders, the real risk is strategic: a try-on gap becomes a ceiling on how far the brand can scale online. What looks like a product-page problem is often a fit, confidence and returns problem in disguise. Every shopper who cannot picture the fit is a basket left half-built. The numbers follow the confidence: higher add-to-cart, fewer bracketed orders, and drops that land. Try-on stops being a gimmick and starts being a default on every product page. Shoppers get a believable look at the fit; the brand gets fewer returns and more confident checkouts.
