Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
Nextail is a cloud-native smart platform for retail merchandising that leverages probabilistic demand forecasting to align supply with hyper-local demand. In the 2026 retail landscape, Nextail serves as the core decision engine for global fashion brands, moving away from legacy deterministic models to a stochastic approach that handles the inherent uncertainty of fashion trends and seasonal cycles. The technical architecture integrates directly with Enterprise Resource Planning (ERP) and Point of Sale (POS) systems via high-throughput APIs, processing millions of SKU-store combinations daily. Its proprietary algorithms automate the 'First Allocation,' 'Dynamic Replenishment,' and 'Store-to-Store Transfers,' ensuring that inventory is always positioned where the highest probability of full-price sale exists. By utilizing advanced prescriptive analytics, Nextail minimizes overstocks by up to 30% and reduces lost sales due to stockouts by an average of 15%, positioning it as a critical infrastructure tool for retailers aiming for operational excellence and sustainability through reduced waste.
Uses Bayesian inference to calculate the probability of sale for every single size and color at every point of sale.
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.
Algorithmic identification of stock imbalances across the network, triggering store-to-store transfers automatically.
Distributes new seasonal stock based on pre-season signals and early sales data in a multi-phased approach.
Deep-learning analysis of size profiles per store to prevent broken-size sets on shelves.
Suggests the optimal timing and discount percentage for slow-moving items based on velocity.
Pull-based system that responds to real-time sales signals rather than fixed schedules.
Quantifies the reduction in carbon footprint achieved through minimized logistics and waste.
Uncertainty on where a new product will perform best.
Registry Updated:2/7/2026
Top-selling items are out of stock in Tier 1 stores but sitting in Tier 3 stores.
Inefficient margin loss due to early markdowns.