Built for fashion brands who'd rather see their own garment on their own customer than guess from someone else's returns.
Most size tools learn from other people's returns. VirtualClothingTryOn.com detects measurements directly from your customer's image and maps them against each garment's specific cut — so even when a brand's sizing shifts between product lines, the recommendation is always based on that garment, that body, that fit. No historical data required. Accurate from day one.
Cheaper try-on tools quietly swap your customer for a generic AI avatar that vaguely resembles them — same garment, different person. We don't. Face, skin tone, hair, posture — preserved. The garment changes; the person doesn't. The result is a try-on that customers actually trust enough to buy from, and share. Every render is a piece of UGC waiting to happen.
Oversized tees, tailored blazers, slip dresses — they don't fit the same body the same way. VirtualClothingTryOn.com parses the garment's silhouette and ease, then pairs it with the detected body. No flat substitution. No 'one size suggestion fits all'.
Zara committed nine figures to virtual try-on infrastructure. The same class of underlying technology now ships to your storefront in a single line of code. No SDK, no rebuild, no 18-month roadmap.
70% of fashion returns are size-related. Brands using VirtualClothingTryOn.com typically see double-digit reductions in size-driven returns and a measurable lift in PDP conversion. The ROI calculator on the home page makes the maths obvious.
We're ex-Google, deeply versed in applied AI and machine learning — the same disciplines that power large-scale computer vision and recommendation systems are what sit under VirtualClothingTryOn.com.
And we know fashion from the inside. In previous roles we've worked with Adidas, New Balance, 424 on Fairfax, and the LVMH Group — so the product is built around how fashion brands actually merchandise, size, and sell, not a generic ML demo retrofitted to apparel.