Snappo
An AI-powered style assistant that identifies clothing items from social media, videos, and images, instantly suggesting exact or similar matches to create a fast, personalized, and seamless shopping experience.
Services
Ideation, UX\UI
Year
2025
Ideation
Developed in collaboration with MBA professionals, CS engineers from Tel Aviv University and the designers Tomer Shahar & Yuval Avizohar. Our team was challenged to identify opportunities for innovation in the field of e-commerce, with a focus on AI-driven shopping experiences. After conducting research into user behaviors, we chose to concentrate on Gen Z — a broad demographic that primarily consumes content through social media yet often struggles to find fashion items they want to purchase. We observed that their discovery process typically relies on influencers, random posts, and trial-and-error searches with imprecise keywords.
From this insight, we explored how to design a faster, more intuitive, and accurate search experience aligned with the natural behavior of scrolling through social feeds. This led to the creation of Snappo.
Product
Snappo is a digital assistant powered by machine learning and advanced image and video analysis. It leverages personalized filters that adapt to user preferences, enabling precise item recognition within its “shopping mode.” With this capability, Snappo instantly locates the exact product a user encounters on their phone. The platform also offers features such as save for later, a styling assistant that suggests complementary items, and seamless direct purchase options from partnered retailers.
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This project was made during the 3rd year of my studies at Shenkar College of Design as part of Design Thinking course.
