Favora
Favora is a fashion-specific discovery engine that turns natural language descriptions and reference images into relevant products from multiple retailer catalogs. The platform lets shoppers compare items side-by-side, refine searches without restarting, and receive personalized daily recommendations that improve with each interaction. It connects user intent directly to product, price, and retailer, with regional availability filtering for local markets.
- Fashion Technology
- Commerce & Shopping
- Artificial Intelligence
- Software Only
- Applications
- Retail Technology
Funding
Funding not disclosed
across 1 round
Founders
Founder details are not available yet.
Product
Problem
Shoppers often struggle to find fashion items when their search starts with an idea, a reference image, or a specific occasion rather than an exact product name. Conventional shopping tools force users to translate this intent into store categories, filters, and repeated searches across multiple tabs, which is time-consuming and often yields irrelevant results. This fragmented process makes it difficult to compare products and track desired items across different retailers.
Solution
Favora provides a unified discovery engine that understands both natural language and images, allowing users to describe an idea or upload a reference photo to find matching products. The platform aggregates catalogues from various retailers, enabling users to compare items side-by-side and refine their search without starting over. Favora learns from user interactions, offering a few personalized new product suggestions each morning and saving all liked items in one place for future purchase. The service connects the user's intent directly to the product, price, and retailer, ensuring a seamless path from discovery to purchase.
Target Audience
The primary audience is fashion-conscious consumers who frequently shop online and are looking for a more efficient, personalized way to discover and track clothing items. The platform also serves brands and retailers, including Nike, Adidas, and Gant, seeking to surface their products to highly engaged shoppers with clear purchase intent.
Features
- AI-powered search that interprets detailed descriptions like "a long black wool coat below 3,000 SEK" and visual inputs from reference images.
- A unified interface for browsing and comparing products from multiple retailer catalogues in one view.
- Personalized daily product recommendations that improve based on user feedback and past interactions.
- A centralized "saved items" feature that allows users to store pieces from any brand and buy them later.
- Regional availability filtering to ensure that search results are relevant to the shopper's local market.
- A browser-based experience planned for both mobile and desktop, grounding search in current products and clear sources.