Virtual try-on
See it on them before they buy
The oldest problem in selling clothes and jewelry online is that a customer cannot picture the thing on themselves — or know whether it will fit. We build both into the moment of decision: a shopper's own photo, your own catalog, and a look they can judge in about a minute.
Hesitation is the thing to engineer against
A shopper who cannot see a garment on their own body does not usually decide no. They decide later — and later is where the sale goes. In store it shows up as “we’ll come back”; online it shows up as a cart with three sizes of the same thing in it.
Both are the same problem wearing different clothes: the customer is being asked to imagine something, at the exact moment they need to be sure of it.
What it does
Your catalog, on your customer
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Reads the customer, then the catalog
From one photo the system reads the outfit, the color and fabric, the neckline and the styling context, and ranks suggestions — drawn only from your own stock, with notes on what to leave off.
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A whole look, not one piece
Up to eight pieces go on in a single render — necklace, earrings, tikka, bangles and the rest — so a customer judges the set they would actually wear, not one item at a time.
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They stay themselves
Every render starts from the untouched original photo rather than pasting the product onto a model, so face, hair, skin tone and posture carry through and two looks can be compared fairly.
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Refine in plain words
“Sit the necklace higher” re-renders from the original. The person selling adjusts it the way they would talk to a stylist, and stays in charge of the sale.
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Catalog onboarding by photo
Drop in product photography and the system drafts the title, category, materials and styling tags. Getting a catalog live is an afternoon's work rather than a data project.
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Saved visits
The photo, the reading and every render are kept against the visit, so a customer who leaves to think about it — or to ask family — is picked up exactly where they left off.
A working prototype, and we mean that precisely
The platform runs end to end today — photo upload, the styling read, catalog-only recommendation, multi-piece try-on, plain-language refinement, catalog onboarding, saved visits and usage tracking, all in the browser on a phone or tablet.
It is a prototype we shape around a client, not a finished product you switch on. We say so plainly because it changes what an early engagement is: you are not buying a fixed feature list, you are deciding what it becomes for your category — and getting something fitted to how your shop actually sells instead of how a generic tool assumes it does.
The same engineering sits behind our AI shopping assistant, which grounds a conversational layer in a real Magento catalog.
Size and fit
Fit is the other half of the problem
Seeing a garment answers “does it suit me”. It does not answer “will it fit” — and that second question is what drives most returns. Parts of this run today and the rest is in active build, which means early clients get a say in which parts come first.
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Measurements without a tape
Body dimensions read from a photo rather than asked for in a form. Shoppers do not know their own measurements, and the figures they report tend to be the ones they wish were true.
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Fit judged against the cut
A size is only right relative to how the garment was designed. A relaxed jacket and a tailored shirt at the same chest measurement are not the same recommendation, so the garment's intended silhouette has to be part of the calculation.
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A reason, not a number
Millimetres persuade nobody. The useful output is a sentence saying why this size and how it will sit — which is also what makes a recommendation reviewable on the occasions it gets it wrong.
How an engagement runs
From your photography to a live pilot
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Pick the category
Jewelry, sarees and ethnic wear, apparel — or something we have not built for yet. Each category's product fields, styling rules and AI instructions live in their own module, so a new one is an addition rather than a rewrite.
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Bring your catalog
Your existing product photography goes in and the onboarding drafts the structured fields around it. No new shoot, no 3D pipeline.
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Fit it to how you sell
In-store kiosk, a stylist with a tablet, a home visit, or a shopper on your own site. The styling logic takes your house rules and the traditions your customers actually buy by.
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Wire it to your systems
Stock sync from your POS or ERP so recommendations follow real availability, lead capture into your CRM, sharing out to the channels your customers use.
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Pilot, then widen
Run it in one store or on one collection, watch what customers ask to see on themselves, and use that — it is merchandising data as much as a selling tool.
Where it fits
Two ways retailers use it
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Assisted selling, in the room
A stylist runs it on a tablet while the customer watches — at the counter, at a bridal appointment, or on a home visit where the whole catalog travels without the stock. High-ticket, high-consideration buying where the decision is emotional and often made by more than one person.
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On your storefront
The same engine behind a product page, so an online shopper can see an item on themselves before adding it to a basket. It integrates with your existing store rather than replacing it — no re-platforming.
Bring us your catalog and one category
The quickest way to judge this is to see your own products on a real customer photo. That is a demo, not a deck.