Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
Transform images into shoppable inventory with industry-leading computer vision and AI-powered product discovery.
ViSenze is a market leader in the 2026 fashion visual search ecosystem, providing a high-performance computer vision API designed specifically for retailers and marketplaces. Its technical architecture utilizes advanced deep learning models (ResNet and Transformer-based backends) to process visual attributes like patterns, textures, and silhouettes with sub-200ms latency. The platform excels at multi-object detection, allowing users to select individual items within a busy lifestyle image. By 2026, the tool has evolved to include 'Contextual Search,' which interprets the setting (e.g., 'summer beach wedding') to refine search results beyond mere visual similarity. ViSenze offers a vertically integrated solution that includes automatic product tagging (V-Tag), search (V-Search), and recommendations (V-Recommend). Its infrastructure is built for massive scale, supporting global catalogs with millions of SKUs and providing enterprise-grade reliability through multi-region cloud deployments. The platform is widely utilized by global retailers like ASOS and Rakuten to bridge the gap between inspiration and purchase, significantly increasing conversion rates through friction-free discovery paths.
Uses localized object detection to identify and isolate multiple apparel items (e.g., shoes, bag, dress) within a single image for individual searching.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The All-in-One AI Marketing Platform for E-commerce Growth and Content Automation.
Transforming legacy open-source e-commerce into autonomous AI-driven storefronts.
The lightweight, high-performance AI engine for rapid e-commerce deployment.
Verified feedback from the global deployment network.
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Extracts over 1,000 attributes from images including sleeve length, neckline type, and material pattern using hierarchical classification.
Goes beyond visual similarity to suggest items based on style consistency and 'Complete the Look' logic.
Dynamic re-ranking of search results based on inventory availability and visual match score.
Automatically triggers a visual similarity search when a user lands on a 404 or an out-of-stock product page.
Combines text-based keywords with visual embeddings for highly specific queries (e.g., 'Blue striped dress' + Image).
Optimized lightweight models that can perform initial feature extraction on the user's device to reduce server load.
Users see an item in real life but don't know the brand or name.
Registry Updated:2/7/2026
User adds to cart.
Translating social media lifestyle images into direct sales.
Massive backlogs of untagged new inventory.