We started with clear data: people do not utilize approximately 80% of their wardrobe (Movinga). Considering the fashion industry's significant environmental impact, this level of waste cannot be overlooked. After studying how people handle their wardrobes, we identified various activities they typically engage in. These activities are plagued by specific pain points and an inefficient user experience, resulting in an ineffective wardrobe management. Consequently, we decided to develop an app that consolidates all the necessary tools for a seamless "fashion customer journey" – from product research to disposal. Our aim is to simplify and enhance this journey. Currently our app provides a comprehensive suite of tools for efficient wardrobe management, like wish-list creation, virtual wardrobe and outfit creation and so on. For example, users can easily add desired products from any website by sharing them with our app, saving crucial details like product page links and images. They can then compare their desired items with each other or with existing pieces in their wardrobe. Moreover, the app's outfit creation tool allows users to match desired products with their current items before making a purchase, enabling them to make informed decisions and acquire only what they truly need. We are now working on developing a generative artificial intelligence system to give even more support to our users. This groundbreaking technology will identify users’ tastes and preferences. As a virtual personal stylist, we will provide users with tailored recommendations for new item combinations based on their existing wardrobe. By leveraging their current possessions, this system encourages users to maximize their existing resources, reducing waste and unnecessary purchases. Additionally, while users evaluate new items, the AI will suggest products based on their existing wardrobe and wish-list, offering them a wider range of options for better decision-making. The app is free for users and we generate revenue through affiliations, retail media, data and engine sales.
