One size, ordered once, and kept.
The try-on layer puts your own garments on the shopper before checkout — on a kiosk in the store, or on their phone at home — so fit and colour stop being a guess and returns stop being the cost of doing business.
FUEiNT Technologies is a software studio in Coimbatore, India, building custom software since 2014. We build virtual try-on for textile retailers and apparel brands, trained on your own catalogue and running on an open-source stack you own outright.
Where the gain is
A shopper who cannot picture the garment on themselves does one of two things: they walk, or they order two sizes and send one back. Both are expensive, and only one of them shows up in your returns figure. Seeing the actual garment on the actual person settles it in the shop — one size, one order, and it stays bought.
What lands, and what you can point at.
Your own product images on the shopper, before checkout. Sarees, formalwear, kidswear, or all of it — we usually start with whichever category comes back most.
Upload a photo and see the garment on someone else, so a gift is chosen with the same confidence as a fitting room.
Alternatives by colour suitability and fit — and, if you want it, by price, so a hesitant shopper is shown something they will actually buy.
The result goes to family in one tap, which is how most of these decisions are genuinely made.
A dashboard of the styles, colours and patterns being tried, shared and bought — the input to your next buying decision.
Run it beside the till and let associates locate the item, or integrate it for inventory lookup and checkout.
How it goes in
We look at the store layout and your existing storefront, and pick the category to start with — usually the one with the worst returns.
The try-on screen is designed for your brand, and for the way people actually queue in your store.
The model is trained on your own garments. Not a stock catalogue, and not somebody else’s stock.
Cloud-hosted for production, with an on-premise proof of concept where the floor needs one.
Regional language on screen, and WhatsApp sharing wired in.
Staff training, then the documentation and the code. Both yours.
Try-on, and the storefront underneath it
The try-on layer sits on a storefront built to load on a mid-range phone on 4G — the device most of your shoppers are actually holding. We build both, and both are open source, so the imagery pipeline keeps up with new stock without a call to us.
Questions we are actually asked.
Kiosk, or the shopper’s own phone?
Both work. A standalone kiosk suits a high-traffic floor; a mobile-assisted flow, where the shopper uses their own device with an associate alongside, suits a smaller shop. Store layout and how people queue usually decide it.
Which garments can it handle?
Any of them — sarees, formalwear, kidswear. We recommend starting with your highest-margin or most-returned category and adding the rest once the effect is visible.
Does it have to touch our POS?
No. It can run standalone and simply recommend products an associate then locates, or it can integrate with your POS for inventory lookup and checkout. Your existing setup decides which is less trouble.
Do we get to see what is trending?
Yes, the analytics dashboard is included by default: which styles, colours and patterns are being tried on, shared and bought, and how that varies by category.
Can it suggest by price as well as by style?
Yes. It can be configured to show more affordable alternatives to a price-sensitive shopper, or premium ones where the interest is clearly there.
Read the proof, or the parts underneath.
Send us twenty products and we will put them on people.
Twenty is enough to see the effect on returns without committing the catalogue. You keep the trained model either way.
Saravanampatti, Coimbatore, TN, India — 641035
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