SeeOnMe provides an AI-powered API that integrates virtual try-on technology directly into apparel e-commerce platforms. This solution allows shoppers to visualize clothing on themselves, which increases purchase confidence and drives higher conversion rates. Retailers benefit from reduced product returns and gain actionable first-party data on shopper engagement and fit preferences.
Funding
Funding not disclosed
Founders
Product
Problem
Online apparel retailers face significant financial losses due to high return rates, primarily driven by customer uncertainty regarding fit and appearance. Traditional methods like size charts and model photography fail to adequately address this issue, leading to increased operational costs and diminished customer satisfaction.
Solution
SeeOnMe provides an AI-powered virtual try-on (VTON) API that integrates seamlessly into e-commerce platforms, enabling shoppers to visualize apparel on a realistic representation of themselves. This technology enhances purchase confidence by providing accurate fit and style previews, thereby reducing return rates and associated overhead for retailers. The platform also captures first-party shopper behavior data, offering actionable insights to optimize merchandising and marketing strategies. By leveraging proprietary machine learning models trained on extensive apparel-body data, SeeOnMe delivers personalized and engaging shopping experiences that drive conversion and foster customer loyalty.
Target Audience
The primary customers are online apparel retailers, including direct-to-consumer (DTC) brands and enterprise e-commerce businesses, seeking to reduce return rates and improve customer conversion.
Features
- AI-driven API for seamless integration with e-commerce platforms (Shopify, WIX, custom builds).
- Generates ultra-realistic virtual try-on visualizations based on a 3D mapping of individual shopper likeness.
- Proprietary machine learning models trained on billions of apparel-body data points for accurate fit and appearance simulation.
- Captures and analyzes first-party shopper interaction data, including try-on frequency and style engagement.
- Provides a retailer dashboard with key performance indicators such as try-on rate, purchase rate, and return rate by SKU.
- Lightweight API designed for fast implementation without disrupting existing e-commerce infrastructure.
- Continuously learning AI model that adapts to style shifts and evolving shopper preferences.