Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
Enterprise-grade predictive intelligence and computer vision for high-velocity fashion retail.
FashionAI by Hitachi is a high-performance retail intelligence suite built on the Hitachi Lumada IoT and AI platform. Engineered for 2026's hyper-fast trend cycles, the system leverages advanced deep learning to synthesize unstructured data from social media, e-commerce, and historical ERP records. Unlike standard forecasting tools, FashionAI utilizes computer vision to 'see' and decompose garment attributes (fabric, silhouette, color) into quantifiable data points. This allows retailers to predict demand at the SKU level with unprecedented granularity. The technical architecture is designed for enterprise scalability, integrating directly with existing PLM (Product Lifecycle Management) and ERP systems to automate inventory rebalancing and markdown strategies. As a leader in the industrial AI space, Hitachi positions this tool for large-scale enterprises looking to mitigate the environmental and financial costs of overproduction through precision demand sensing. The platform's 2026 roadmap emphasizes generative design integration and circular economy tracking, ensuring that fashion brands can maintain agility while adhering to tightening global sustainability regulations.
Uses convolutional neural networks (CNNs) to identify over 500 distinct garment attributes from raw product images.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The world's premier wholesale management platform powered by predictive AI and global data insights.
Photorealistic 3D customization and spatial visualization for bespoke furniture design.
Architect-grade e-commerce storefronts generated via specialized LLMs for high-conversion retail.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Reinforcement learning models that calculate the price elasticity of individual SKUs to suggest optimal discount timing.
Algorithmic redistribution of inventory between physical stores and e-commerce hubs based on localized demand signals.
Natural Language Processing (NLP) and Image Recognition combined to scan social platforms for emerging micro-trends.
Calculates the carbon footprint reduction achieved through minimized overproduction and optimized logistics.
Leverages Hitachi's proprietary IoT backbone for high-availability data processing and edge computing capabilities.
Analyzes return data and customer feedback to identify size-chart discrepancies and fit issues early in the season.
Designers and buyers often rely on 'gut feeling,' leading to high volumes of unsold stock.
Registry Updated:2/7/2026
One store is sold out of a high-demand jacket while another store has 50 units sitting idle.
Identifying a viral trend too late to capitalize on the production cycle.