BeWear

The Ethical Fashion Intelligence Platform

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Inspiration

Fast fashion contributes to unethical labor practices and environmental damage, yet it remains hard for consumers to identify which brands are sustainable. We wanted to make ethical awareness easy as taking a photo. BeWear was inspired by the idea of merging visual recognition and AI-driven transparency to empower everyday consumers to make responsible choices.

What it does

BeWear allows users to snap a picture of any branded textile or clothing item (or manually enter the brand). The app then detects the brand from the image and retrieves an "ethical score" that is calculated using different variables representing the brand's sourcing, labor, environmental impact, transparency, and more. BeWear gives you an in-depth summary of the variables that went into calculating the "ethical score", and users have the option to use an agent to scrape for real-time events related to greenwashing.

How we built it

Frontend: Vanilla JS Data Layer: Elastisearch

  • single index, fuzzy matching, typo tolerance, >6000 brands ingested from scraped data Backend: Python
  • SerpAPI(Google Lens API) for Visual brand identification
  • ImgBB for image hosting for google Lens analysis
  • Anthropic (Claude) API for brand extraction, analysis, and use in agent
  • LangGraph for agent workflow orchestration for greenwashing investigation
  • Tavily API for AI optimized web search
  • SSE: real-time status streaming to frontend
  • Agent decision logic ## Challenges we ran into
  • LangGraph state management: there was a bug where part of the state (articles and their respective links) was not mapping correctly, and the agent kept going to the same links.
  • Scraping through 6000+ pages: populating the database with every single brand in the good on you website (essentially we create a MCP for good on you API) ## Accomplishments that we're proud of
  • Completed a database of 6000+ brands
  • Easy-to-use UI
  • AI Powered Greenwashing detection ## What we learned
  • We learned how to manage stress under high constraints
  • We learned how to fully integrate front and back end
  • We learned how to obtain real-time updates using SSE
  • We learned how to work efficiently by dividing work within the team ## What's next for BeWear
  • We will turn BeWear into a browser extension allow it to assist the user during shopping
  • User reviews
  • ML Based similar brand detection

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