Whole-store try-on
Your customer creates a Digital Twin once — two photos, ninety seconds — and from then on every product image across your whole store shows them wearing it, not a model.
Why it matters: shoppers who can see themselves in the product buy with confidence. Virtual try-on lifts conversion by 10–23%.
Mirror Mode
A single toggle flips any page between your campaign photography and the customer wearing the piece. They're always in control of what they see.
Why it matters: your brand vision stays intact — you add a personal layer on top, lifting engagement without compromising your art direction.
See it in your size
Measurements are read from the customer's photos and every garment is rendered at their real size — a size-14 shopper sees a size-14 body — with a size recommendation on every product page.
Why it matters: fit drives up to ~70% of apparel returns. Showing true-to-size fit plus a clear recommendation is the highest-leverage way to cut them.
Two ways to build a Twin — no photo needed
Customers can upload two photos, or build their avatar from their measurements — no photo needed. Upload photos and their measurements are calculated from the images; build from measurements and they type them in — either way the Twin carries real measurements, which is exactly what powers the Fit Advisor on every product page.
Why it matters: the photo upload is the single biggest drop-off in any try-on funnel. Shoppers who won't send a photo — for privacy, or because they just can't be bothered — can still get a Twin and still see themselves in your store.
Quick Try-On for guests
Not logged in? A guest uploads one photo and sees a product on themselves in under a minute — no app, no account.
Why it matters: it converts first-time visitors (try-on lifts conversion 10–23%) and captures a consented name and email — turning anonymous traffic into marketing leads.
Accessories & your own outfit
Shoppers upload a photo in the outfit they're already wearing and your jewellery, bags, shoes, scarves, eyewear and watches are added onto it — each placed naturally.
Why it matters: accessories finally shown in real context mean fewer "doesn't match" returns and more confident add-on purchases.
The Visual AI Stylist Concierge
An AI stylist that shows instead of tells — it recommends, restyles, changes the scene and adds the whole outfit to cart, all on the customer's own try-on.
Why it matters: recommendations drive ~31% of e-commerce revenue. A recommender shoppers can see on themselves converts far better than a text suggestion.
Complete the Look
Every recommended piece is shown already on the customer as a full outfit — one tap to try, one tap to cart.
Why it matters: it sells outfits, not items. Virtual try-on lifts average order value by 12–18%.
See yourself anywhere
With a single sentence the customer can change the entire backdrop and see themselves — in your clothes — on a beach, in a bar, a restaurant, the streets of Paris or a rooftop in Milan.
Why it matters: lifestyle context sells the moment, not just the garment — unlimited editorial imagery without booking a single shoot.
Their own Twin dashboard
Customers get their own dashboard: update their measurements as their body changes, manage their Twin, and see the other stores on the network where it already works.
Why it matters: a Twin that stays accurate keeps sizing accurate — and the network view turns your customer into a discovery surface for the whole cohort, and brings network shoppers back to you.
My Looks — their own lookbook
Every look a customer creates on any store in the network saves to their own lookbook in their dashboard — Recent (their latest try-ons, automatic) and Saved (kept to revisit any time). They build their own outfits and looks there, and when they're ready, the images take them straight back to your store — Shop this look adds the in-stock pieces to cart at their size, with live stock shown on every piece.
Why it matters: the customer gets a personal lookbook of themselves across every brand they shop — and you get measurable return traffic delivered from the network: "looks saved" becomes "came back and bought". It costs nothing to serve — saves are bookmarks of renders that already exist.
