Lily AI

Lily AI

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Lily AI is a product attribution and discovery platform that uses computer vision and customer-centric language to improve retail search and recommendations.

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Lily AI
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📋 About Lily AI

Lily AI is a retail and e-commerce platform that applies computer vision and natural language processing to enrich product catalogs with thousands of consumer-centric attributes. Traditional product taxonomies are built by merchandisers, but shoppers describe items in their own words — 'flowy', 'cottagecore', 'great for office' — that rarely match back-office tags. Lily closes that gap by auto-generating the attributes customers actually search for, improving conversion across search, PLPs, and recommendation surfaces.

Key Features of Lily AI

1

Consumer-Centric Attribute Generation

Lily AI auto-generates thousands of attributes per product using the language real shoppers use, not internal taxonomy. Products are tagged with descriptors like 'date night', 'petite friendly', or 'coastal grandmother' so search and recommendations align to how customers actually shop. Retailers unlock long-tail demand that internal taxonomies miss entirely.

2

Computer Vision on Product Imagery

A specialized vision model analyzes product photos to extract color, pattern, silhouette, material, and style cues without needing human review. This means images alone can power rich catalog enrichment, even when supplier data is incomplete. Accuracy is tuned to retail-specific categories rather than generic web images.

3

Search Relevance Lift

Enriched attributes flow directly into the retailer's on-site search engine, producing measurable lifts in click-through rate, conversion, and zero-result-rate reduction. Lily quantifies this with before-and-after dashboards so teams can prove ROI. Many customers see double-digit percentage gains in search-driven revenue.

4

Paid Media and Shopping Feed Optimization

Attribute-enriched feeds improve performance on Google Shopping, Meta Ads, and other paid media channels by better matching user queries and preferences. Campaign ROAS rises because ads reach more relevant audiences with more descriptive creative. Retailers often see a fast payback from this channel alone.

5

SEO and Organic Traffic Boost

Product pages enriched with consumer-centric language rank better for long-tail organic searches. Lily's attributes are deployed in page copy, meta tags, and structured data to lift organic traffic to category and PDP pages. This is especially impactful for retailers with large, fast-moving catalogs.

6

Merchandising Intelligence

Analytics dashboards reveal which attributes drive conversion, which inventory categories are underserved, and how customer language is shifting over time. Merchandisers use these signals to plan assortments, promotions, and marketing campaigns. This turns catalog data into a strategic planning tool.

🎯 Use Cases for Lily AI

Large apparel retailers enriching millions of SKUs automatically to capture long-tail search demand their internal taxonomy missed, boosting on-site search revenue significantly. Home goods brands improving Google Shopping ROAS by deploying Lily-generated attributes into their product feeds, so ads match shopper vocabulary more closely. Beauty retailers using consumer-centric tags like 'mature skin' or 'fragrance-free' to power filters that shoppers actually use, reducing frustration and lifting conversion. SEO teams at multi-brand marketplaces gaining organic traffic by updating PDP copy and structured data with Lily's enriched language across product categories. Merchandising leaders analyzing which attributes are winning with shoppers to guide buying, promotion, and seasonal campaign planning.

⚖️ Lily AI Pros & Cons

Advantages

  • Closes the gap between shopper language and back-office taxonomy
  • Enriches entire catalogs without manual tagging
  • Measurable lifts across search, paid media, and SEO
  • Specialized models for apparel, home, and beauty
  • Integrates with major commerce and search platforms

Drawbacks

  • Enterprise-focused pricing not suited for small shops
  • Best results require full catalog access for enrichment
  • Requires integration work to deploy attributes broadly
  • Category coverage is strongest in fashion and home

📖 How to Use Lily AI

1

Contact Lily AI sales to scope catalog size and target retail category.

2

Share product images, descriptions, and existing taxonomy via secure integration.

3

Review the enriched attribute set and approve any custom categories.

4

Deploy attributes into your search engine, product feed, and SEO surfaces.

5

Measure lift with Lily's before-and-after dashboards.

6

Iterate on attribute coverage and monitor merchandising intelligence over time.

Lily AI FAQ

Apparel is the strongest category, followed by home goods, beauty, and general merchandise. New categories are added regularly.

Most enterprise retailers see enriched catalogs within weeks, with full deployment across search, feeds, and SEO in one to three months.

No. Lily enriches the data that powers your existing search, recommendations, and ads. It works alongside Algolia, Constructor, Bloomreach, and similar tools.

Pricing is enterprise and varies by catalog size and channels activated. Contact sales for a quote.

Yes. Lily provides dashboards that quantify lifts in search conversion, paid media ROAS, and organic traffic so you can attribute gains directly.

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