Master Lily AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Contact Lily AI sales team at lily.ai/contact to schedule a demo and discuss catalog size, current technology stack, target channels, and expected business outcomes for a tailored implementation plan. Export your product catalog in CSV format with product images, existing descriptions, category hierarchy, and any current attribute data to share with Lily AI's onboarding team for initial taxonomy assessment. Complete API integration setup by following Lily AI's technical documentation to connect your product catalog feed, search platform, and target output channels for enriched attribute delivery. Configure attribute mapping dashboard to translate Lily AI's enriched taxonomy into your platform's required attribute schema, validating output quality across a sample product set before full deployment. Launch A/B testing on 10
20% of traffic comparing enriched versus original product attributes to measure impact on search relevance, click
through rates, and conversion before scaling to full catalog coverage.
💡 Quick Start: Follow these 3 steps in order to get up and running with Lily AI quickly.
Explore the key features that make Lily AI powerful for content & seo workflows.
AI that interprets natural consumer language and maps it to product attributes, ensuring that product listings match the vocabulary shoppers actually use when searching for fashion, home, and beauty items rather than relying on internal merchandising terminology.
Customer searches for 'comfy work pants that stretch' and Lily AI's enriched attributes ensure relevant products surface because they've been tagged with consumer-language terms like 'comfortable,' 'workwear,' and 'stretch fit' rather than only internal descriptors like 'trouser' and 'elastane blend.'
Machine learning that understands individual customer preferences at the product attribute level, enabling personalized recommendations based on style, fit, material, and aesthetic preferences rather than simple collaborative filtering or purchase history alone.
Customer who consistently purchases relaxed-fit linen clothing in earth tones receives personalized recommendations emphasizing similar silhouettes, natural fabrics, and warm neutral colors across categories, even for product types they haven't browsed before.
AI-powered merchandising that automatically optimizes product ranking, category page ordering, and collection curation based on enriched attribute data combined with real-time consumer demand signals and trending search patterns.
Platform automatically promotes lightweight linen dresses and breathable cotton tops on category pages as spring search queries spike, using enriched seasonal and fabric attributes to align merchandising with current consumer demand.
Detailed insights into customer search behavior, attribute engagement patterns, and conversion pathways that help retailers understand which product characteristics drive discovery and purchase decisions across channels.
Retailer discovers that customers who find products through enriched long-tail search terms like 'mid-century modern oak bookshelf' convert at 3x the rate of generic category browsers, justifying further investment in attribute enrichment for the home goods catalog.
Lily AI uses AI models trained on millions of consumer search queries and product images to generate rich, consumer-centric product attributes. These attributes align product listings with the language shoppers actually use, improving relevance in onsite search, organic SEO, Google Shopping feeds, and recommendation engines. By bridging the gap between internal merchandising terminology and consumer vocabulary, Lily AI ensures products surface for the queries most likely to convert.
Retailers typically see incremental organic traffic increases of 20-40%, conversion rate improvements of 5-9%, and measurable lifts in Google Shopping and Performance Max campaign performance within 60-90 days of full deployment. Results vary based on catalog size, existing content quality, and the breadth of channels receiving enriched attributes. Lily AI provides performance reporting tied to traffic, conversion, and revenue metrics so teams can quantify ROI.
Yes, Lily AI's Application Layer architecture is designed to integrate with existing ecommerce technology stacks rather than replace them. The platform connects with major search providers, PIM systems, content management platforms, and advertising channels through APIs and pre-built connectors. Retailers can layer Lily AI's attribute enrichment onto their current infrastructure without migrating away from existing tools.
Lily AI specializes in fashion, apparel, home goods, furniture, and beauty retail categories. The platform's taxonomy and computer vision models are purpose-built for these verticals, with thousands of category-specific attribute values covering silhouette, pattern, texture, material, color, style, occasion, and other dimensions relevant to how consumers search for these products. The platform does not currently support non-retail verticals such as electronics, grocery, or automotive.
Lily AI continuously monitors millions of consumer search queries, social signals, and emerging style terminology to keep its attribute models current. The platform identifies trending language patterns—such as new style descriptors, seasonal terms, or viral product characteristics—and incorporates them into product attribute enrichment. This ensures that product listings remain aligned with how consumers are actively searching, rather than relying on static taxonomies that become outdated as language evolves.
Now that you know how to use Lily AI, it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful content & seo tool in minutes.
Tutorial updated March 2026