FitSnap

FitSnap is an AI driven web application, designed to enhance and personalize online shopping experience by curating recommendations based on the user’s individual style preferences. Users create customizable “closets” by inputting product detail page (PDP) URLs of clothing items they’ve found while browsing online. Each URL is analyzed by AI using content-based filtering and feature extraction, identifying key clothing attributes to generate personalized recommendations for similar styles.

By processing product images and descriptions in real-time, FitSnap eliminates the need for manual search and serves as a personal shopper. As users add more items, the system refines its keyword-based suggestions, increasing recommendation accuracy and allowing room for self-discovery within the user’s evolving style.
FitSnap is generally designed for shoppers who view shopping as a leisure activity, but is also ideal for anyone with a defined style, for those exploring new fashion directions, or for users assembling outfits for special events or occasions.

Product Demo

Target Users

AI Workflow

When a user inputs a PDP url, the backend initiates the process by sending the url to a web scraping script. This script extracts all available text and image content from the specified product page.

Using feature extraction, artificial intelligence parses out the product details such as name, price, related tags from the inputs. The tags are saved and uses content-based filtering to do a web search based on all the product details.

This outputs product recommendations, including their associated details. These curated recommendations are then presented to users as a browsable recommended product catalog, offering personalized browsing and shopping experience.

User Flow Diagram

Product Roadmap

Business Trajectory

FitSnap acts as a three-way relationship between its platform, its users, and various brands, creating a mutually beneficial ecosystem. Users receive personalized product recommendations, improved brand discovery, and reduced returns, while brands gain valuable user insights to optimize production, marketing, and inventory decisions. FitSnap stores engagement data and monetizes through brand partnerships, offering premium visibility within its AI-driven recommendation system. Brands can access advanced analytics and gain priority exposure through either subscription tiers or CPM (Cost Per Thousand) models. While FitSnap’s current recommendations rely on web-scraping, direct brand partnerships will allow FitSnap to provide more accurate, comprehensive suggestions, benefiting users, partner brands, and the platform itself.

For project inquiry, please contact ashleyleejunior97@gmail.com or leea679@newschool.edu