CoSupport AI
What is QuickList?
Leverage artificial intelligence to transform photos of used items into compelling sales listings. This tool aims to simplify the often tedious process of selling pre-owned goods by automatically generating professional descriptions, assessing item conditions, suggesting market-based pricing, and creating SEO-optimized titles. It helps users uncover the hidden value in everyday household items, making online selling more efficient.
Designed with user convenience and privacy in mind, it formats listings specifically for platforms like Facebook Marketplace. A unique 'Discovery Mode' assists users in identifying potentially valuable items during activities like antique hunting or visiting yard sales by providing historical context and value estimation. All listing information is stored directly on the user's device, ensuring data privacy as no user data is collected externally.
Features
- AI Listing Generator: Instantly creates professional descriptions, detailed condition assessments, and SEO-optimized titles from photos.
- Market-Based Pricing: Suggests appropriate prices based on current used items market data.
- Discovery Mode: Helps identify valuable items at sales and provides insights into their history and worth.
- Formatted for Facebook Marketplace: Generates listings ready to be posted on FB Marketplace.
- Performance Tracking: Allows users to visually track the value of their listings.
- Private & Secure: Stores all listing data locally on the user's device, collecting no user data.
Use Cases
- Creating optimized listings for selling used items online.
- Generating descriptions and titles for Facebook Marketplace.
- Estimating the resale value of pre-owned goods.
- Identifying valuable finds at yard sales and antique shops.
- Streamlining the process of selling unwanted household items.
- Selling specific collectibles like LEGOs, trading cards, or vintage clothing.
Related Queries
Helpful for people in the following professions
QuickList Uptime Monitor
Average Uptime
100%
Average Response Time
109.57 ms
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