Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
Enterprise-grade AI infrastructure for virtual try-ons, automated cataloging, and hyper-personalized retail experiences.
The FashionAI System represents a pinnacle of retail-focused computer vision and generative AI technology. Engineered for the 2026 retail landscape, the system utilizes a proprietary Vision Transformer (ViT) architecture specifically fine-tuned on the 'FashionAI Global Dataset' (originally conceptualized by Alibaba and now expanded through synthetic data generation). It serves three core operational pillars: automated high-precision attribute tagging, 3D-to-2D virtual try-on simulation, and visual search recommendation engines. The 2026 iteration introduces 'Neural Fabric Simulation,' allowing the engine to realistically predict how different textile weights (e.g., 12oz denim vs. silk) drape over diverse body archetypes in real-time. By integrating directly with headless commerce APIs (Commercetools, Shopify Plus, BigCommerce), FashionAI System bridges the gap between static product imagery and interactive customer journeys. Its architecture is optimized for low-latency inference at the edge, ensuring that high-fidelity virtual fittings occur in under 800ms, a critical threshold for mobile conversion rates. The platform's market position is characterized by its shift from simple image filters to a full-stack data intelligence layer that predicts inventory demand based on visual trend popularity.
Uses physics-informed neural networks (PINNs) to simulate realistic garment drape and movement on 3D avatars.
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.
Post queries, share implementation strategies, and help other users.
Leverages large multimodal models (LMMs) to identify over 1,000 distinct fashion attributes without per-client training.
Generates photorealistic human models of any ethnicity, age, or size to showcase garments.
Mobile SDK that allows users to upload 'street style' photos to find matching items in the retailer's inventory.
Utilizes standard smartphone cameras to extract 30+ body measurements with 98% accuracy.
Analyzes real-time social media visual data to suggest inventory adjustments.
Automatically recommends complete outfits (Mix & Match) based on aesthetic compatibility scoring.
High return rates due to 'fit' uncertainty in online shopping.
Registry Updated:2/7/2026
Purchase with confidence
Manual data entry for thousands of new SKUs is slow and prone to error.
Consumers see styles they like in public but cannot describe them effectively in text search.