TuuinAI offers body‑accurate virtual fitting technology that lets online shoppers see how clothing will look on a digital replica of their own body. By integrating AI‑generated 3D avatars directly into e‑commerce platforms, the solution reduces return rates and improves purchase confidence for apparel retailers. The service supports real‑time try‑on experiences across web and mobile, using customers’ measurements to create personalized, lifelike fit visualizations.
Funding
Funding not disclosed
Founders
Product
Problem
Online apparel retailers struggle with high return rates because shoppers cannot accurately assess how garments will fit their individual bodies, leading to dissatisfaction and increased operational costs.
Solution
TuuinAI offers an AI-powered virtual fitting platform that creates a precise 3D replica of a shopper’s body using computer‑vision analysis of photos or video. The model is body‑accurate, reflecting detailed measurements such as bust, waist, hips, and length. Retailers integrate the solution into their e‑commerce sites, allowing customers to try on clothing virtually and see how each item drapes on their personal avatar. The platform updates fit predictions in real time as users select different sizes, providing visual confidence before purchase. By delivering a realistic fit preview, TuuinAI helps reduce return rates and enhances the overall online shopping experience.
Target Audience
Primary customers are online apparel retailers and fashion brands seeking to lower return rates and improve conversion by offering personalized virtual fitting experiences.
Features
- Computer‑vision engine that extracts full-body measurements from user‑submitted images without manual input
- Generation of a high‑fidelity 3D body model that accurately reflects individual shape and proportions
- Real‑time virtual try‑on visualization for any garment in the retailer’s catalog
- Size recommendation algorithm that suggests optimal sizes based on the user’s body model
- Easy integration via API or SDK for seamless embedding into existing e‑commerce platforms