Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
Autonomous E-commerce Growth Engine for Amazon and Walmart Marketplaces.
Xena Intelligence (MeetXena) represents the 2026 frontier of autonomous commerce, moving beyond simple rule-based automation into a fully agentic decision engine. The technical architecture leverages a proprietary multi-agent system that ingests high-velocity retail data—including real-time bidding signals, inventory levels, and competitor price fluctuations—to execute granular campaign adjustments without human intervention. By 2026, Xena has positioned itself as the 'Auto-Pilot' for omnichannel brands, specifically bridging the gap between Amazon Seller Central and Walmart Marketplace. Its core value proposition lies in its 'Inventory-Aware Bidding' logic, which dynamically throttles ad spend based on real-time stock-out risks and logistical lead times, ensuring that marketing capital is never wasted on products with fulfillment bottlenecks. The platform integrates deep learning models to predict 'Share of Voice' trends, allowing brands to preemptively capture high-intent traffic before manual operators can react to market shifts. For the enterprise architect, Xena offers a robust data pipeline that transforms fragmented marketplace reports into a unified JSON-based profit-and-loss dashboard, facilitating rapid C-suite decision-making and cross-channel budget allocation.
Direct integration with FBA/WFS inventory APIs to pause or reduce bids when stock levels fall below 14-day trailing velocity thresholds.
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.
Uses LLM-based processing to identify long-tail search terms that are contextually relevant but not present in current ad copy.
Scrapes SERPs in real-time to calculate percentage of organic vs. paid presence for specific product categories.
Adjusts bids based on hourly conversion rates rather than daily averages.
Unified data model that tracks customer journeys across Amazon and Walmart to prevent double-counting of sales.
Machine learning models that account for seasonality and ad-driven velocity to predict precise restock dates.
Automatically targets competitor ASINs with lower ratings or higher prices to steal market share.
High initial costs and slow indexing for new Amazon products.
Registry Updated:2/7/2026
Bids are pushed to gain 1st-page visibility within 72 hours.
Profit margins dipping due to high CPC and low conversion rates in off-peak months.
Wasted ad spend when products are close to stock-out.