Meshada
AI-driven style curation and aesthetic discovery.
What needs solving?
Online apparel shopping is plagued by overwhelming catalogs and search engines that only understand literal text keywords. They cannot comprehend visual style, fit context, or a user's unique visual identity.
How Meshada delivers
Meshada changes search from keywords to aesthetics. Through visual swipe-to-rate loops, our system learns your personal style taste dynamically, curating outfits from global retail databases that match your fit and aesthetic profile.
System Specs
Key Capabilities
- //Dynamic style swipe feedback loops
- //High-dimensional visual feature extraction
- //Vector-based recommendation mapping
- //Real-time retail inventory synchronization
- //Interactive digital wardrobe curation
Tech Stack
Meshada utilizes a dual-model recommendation system. First, a PyTorch-based computer vision model processes apparel imagery, extracting high-dimensional feature vectors representing styles, patterns, cuts, and colors. Second, a collaborative filtering algorithm maps these features against user interaction patterns in a vector database. The app is built with cross-platform frameworks for high-fidelity UI animations.
Know what to build before you pay to build it.
Send us the rough idea. Within one business day, a technical lead will reply with a practical first-pass build plan.
- The smallest useful first release
- A realistic delivery and budget range
- The biggest technical risk to solve first
Project intake