Fancy that fashionable dress on the actress or the sleek pants worn by a celebrity? If you’re wondering where to get one of similar design, you can now do so following the launch of StyleCrush in Singapore.

Created by Korea’s Odd Concepts, the style discovery platform lets consumers leverage AI technology to find clothes and fashion items, including shoes and bags, that caught their attention — establishing a new way of online consumption.

With the platform,  consumers collect fashion style images according to their tastes in a digital album and the AI technology assists them in searching for and purchasing similar products.

All it takes is to upload an image or paste a link and the AI system will connect consumers to the relevant shopping malls where they can purchase the products conveniently.

Consumers can create a wish list of the fashion products discovered through the search and create an album with their preferred style images.

Partnership with Qoo10

Odd Concepts collaborates with Suggesty and Sta1 of Korea, and Qoo10 of Singapore to help consumers discover and enjoy a greater variety of fashion styles on StyleCrush.

Suggesty is an online mall selling AI-curated Korean designer brands internationally and will share its database and apparels with StyleCrush. Sta1 has more than one million products from over 6,000 brands.

Odd Concepts will work with more fashion and apparel e-commerce partners in Singapore and ASEAN countries to provide consumers with access to a greater variety of fashion styles.

“StyleCrush reflects a new wave of needs which arises from the next generation consumers, amid the radical shift in the online fashion industry. AI assisting fashion discovery solely from a user style will become a new norm for the coming years. We look forward to connecting online shopping malls to style wannabes across Asia,” said Brian S Bae, Chief Strategy Officer of Odd Concepts.

Through its acquisition of Singapore startup Seacrux, Odd Concepts has been promoting contextual targeting ad business based on applied machine learning.