Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
AI-driven size and fit recommendations to eliminate returns and boost fashion conversion.
Dresslife is a high-performance AI personalization platform specifically architected for the global fashion retail sector. Its core engine utilizes proprietary machine learning models that analyze the interaction between garment specifications, consumer body profiles, and historical purchase/return data. In 2026, the platform has matured into a predictive analytics powerhouse, offering retailers a 1:1 personalization layer that goes beyond simple measurements to incorporate style preference and 'fit feel.' The technical architecture is built on a high-availability API-first structure, allowing for seamless integration into headless commerce environments and traditional monoliths alike. By providing highly accurate size recommendations, Dresslife addresses the industry's most significant overhead: the high rate of returns. Its data-driven approach allows for the creation of 'Digital Twin' profiles for consumers, which evolve with every interaction. This enables retailers to optimize inventory management and reduce the carbon footprint associated with logistical churn. As a market leader in 2026, Dresslife provides a critical bridge between sustainable practices and operational profitability, positioning itself as an essential component of the modern fashion tech stack.
Uses NLP and computer vision to extract over 50 fit-critical attributes from product descriptions and images.
The semantic glue between product attributes and consumer search intent for enterprise retail.
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Combines physical body measurements with behavioral data (e.g., brands the user kept vs. returned).
Automatically normalizes inconsistent sizing across different third-party brands on a single platform.
Predicts the probability of a return for a specific item-user pair before the purchase is finalized.
Generates a 2D/3D overlay of how a specific size will fit on the user's estimated body shape.
Aggregates fit data to advise retailers on which sizes to over-stock or under-stock for future seasons.
Calculates the CO2 reduction achieved by preventing return shipments and repackaging.
A multi-brand retailer suffers from customer confusion because 'Medium' varies across 500 different brands.
Registry Updated:2/7/2026
A fast-fashion brand sees a 40% return rate on denim products due to fit issues.
Low click-through and conversion rates on 'New Arrival' emails.