Lily AI Review (2026): Features, Pricing & Verdict
Lily AI is an enterprise product attribute intelligence platform whose core mission — bridging the gap between how retailers describe products (merchant-speak) and how consumers actually search for them (consumer-speak) — sits at the intersection of catalogue data enrichment, AI-driven discovery and the emerging imperative to make product content legible to the growing ecosystem of agentic AI shopping surfaces. Founded by Purva Gupta, Lily AI works with some of the most recognised apparel and lifestyle retailers in the United States: Tapestry (Coach, Kate Spade, Stuart Weitzman), Macy’s, Bloomingdale’s, J.Crew, Abercrombie & Fitch, Tory Burch, Fabletics and thredUP. The platform is trained on 300+ fine-grained classification models and over 3 billion retail and consumer data points, enabling it to enrich product catalogues with 15,000+ consumer-centric attributes — from objective descriptors such as materials, construction and fit, through subjective consumer language like occasion, trend association, aesthetic style and micro-cultural references (Barbiecore, Mob Wife, Blokette) — at a scale and consistency that manual tagging teams cannot match. The commercial outcomes documented by Lily’s clients are among the strongest in Cat 23: 5–22% sales lifts in Google Ads, a 28% revenue lift for a large apparel brand on Google Shopping (matched-spend A/B test), a 21.4% ROAS lift for a national footwear retailer on Meta (matched-spend with holdout group, cross-validated against Meta Conversion Lift Study), and 5–25% improvements in clicks, impressions and conversions across channels. Tapestry’s use of Lily to enrich Coach’s Tabby Shoulder Bag — adding dozens of new consumer-facing descriptors aligned with real shopper search behaviour — delivered double-digit improvements across conversions, average sales per view and SEO metrics. Lily Max, the 2026 flagship, is the platform’s evolution into an agentic product intelligence engine that enriches feeds, schema and bot-facing content specifically to make products discoverable on the growing range of AI commerce surfaces — Google AI Overviews, Meta Shopping, onsite AI search, ChatGPT and Gemini shopping integrations. Free 30-day trial on 500 products via Lily Max; all other pricing enterprise custom.
- Best for
- Enterprise apparel, beauty and lifestyle retailers needing AI product attribute enrichment at catalogue scale to lift performance across paid, organic and AI discovery channels
- Clients
- Tapestry (Coach/Kate Spade/Stuart Weitzman) · Macy’s · Bloomingdale’s · J.Crew · Abercrombie & Fitch · Tory Burch · Fabletics · thredUP
- Core results
- 5–22% Google Ads sales lift · 28% Google Shopping revenue lift · 21.4% Meta ROAS lift · 10–15% onsite conversion lift
- 2026 flagship
- Lily Max — agentic product intelligence engine with 30-day trial on 500 products
- Pricing
- Enterprise custom · 30-day trial (500 products) via Lily Max
What Is Lily AI?
Lily AI is a retail-specific AI platform that enriches product catalogues with consumer-centric attributes at enterprise scale. Where most ecommerce operations rely on product data written by merchants and buyers — precise about SKU-level specifications, imprecise about the language real shoppers use — Lily’s AI analyses product images and descriptions alongside clickstream behavioural data, then enriches each product with the attributes, synonyms, occasion tags and trend references that consumers actually use when discovering products. The result is product content that performs better across every discovery channel that consumes it: onsite search returns more relevant results, paid shopping feeds (Google, Meta) match more relevant queries, AI discovery surfaces (ChatGPT, Gemini, Google AI Overviews) can understand and recommend the product, and organic SEO improves as product pages align with real consumer search terms.
Core Features
15,000+ consumer-centric attributes — the vocabulary layer
Lily’s core technical output is a rich set of consumer-centric product attributes generated by a combination of computer vision, generative AI, NLP, machine learning and deep learning — applied to product images and existing descriptions. The 15,000+ attribute vocabulary covers the full spectrum of what consumers care about when shopping: objective descriptors (materials, construction details, fit, silhouette, closures, care instructions), subjective consumer language (occasion suitability, aesthetic style, trend associations, mood, season), synonyms that bridge terminology gaps between merchant and consumer language (glossy vs. high shine, blouse vs. top, trainers vs. sneakers), and micro-trend references updated continuously by Lily’s internal team of retail experts and stylists (Barbiecore, Mob Wife Aesthetic, Quiet Luxury, Blokette, and similar culturally current references that consumers actively search). This vocabulary is not static: Lily’s internal trend-tracking team continuously reviews what consumers are searching, discovers emerging terminology before it peaks, and adds it to the enrichment models — ensuring retailer product content remains current with shopper language in real time rather than requiring quarterly manual updates. For multi-seller marketplace operators, Lily normalises taxonomy and attributes across all third-party seller feeds automatically — producing a unified shopping experience regardless of how inconsistently individual sellers have described their products.
Lily Max — agentic product intelligence for AI commerce
Lily Max is the 2026 flagship product and represents Lily’s evolution from a catalogue enrichment platform into an agentic product intelligence engine purpose-built for AI commerce surfaces. Using goal-based AI agents, Lily Max enriches product feeds, schema and bot-facing content so that AI systems — Google AI Overviews, Meta’s AI shopping integrations, ChatGPT’s product recommendations, Gemini’s shopping surfaces, and retailer-owned onsite AI search — can understand, match and recommend the enriched products. The distinction from traditional catalogue enrichment is important: Lily Max structures product data as machine-readable, schema-validated payloads that AI systems can process at the level of specificity required to include a product in an AI-generated answer or recommendation — not just improving keyword matching, but making the product legible to AI reasoning engines that evaluate structured product attributes when generating recommendations. Lily Max runs controlled A/B tests to validate which enrichments actually lift performance before they are deployed at scale — comparing Lily-enriched versus baseline product feeds against matched-spend control groups across Google Ads, Meta Ads and onsite search. This test-first approach means retailers receive validated commercial evidence of the enrichment value before committing to full catalogue deployment. The 30-day trial on 500 products is structured to produce this evidence within a single trial period.
Multi-channel performance impact — paid, organic and AI discovery
Lily AI’s commercial case is built on cross-channel performance data from its enterprise client base. On paid channels, the documented results are substantial: Google Ads sales lifts of 5–22% across client deployments, with a specific large apparel client achieving a 28% revenue lift on Google Shopping in a matched-spend A/B test. On Meta, a national footwear retailer achieved a 21.4% ROAS lift at near-identical spend levels, validated by both a matched-spend holdout group and a cross-validated Meta Conversion Lift Study — the most rigorous attribution methodology available on the Meta platform. On onsite search, attribute enrichment delivers 10–15% conversion rate lifts as shoppers discover products they would not have found through keyword-only matching. The Tapestry/Coach Tabby Shoulder Bag case study is the most detailed published: Lily enriched the bag’s product listing with dozens of new consumer-facing descriptors (occasion suitability, style references, material synonyms, trend associations) aligned with real consumer search patterns, resulting in double-digit improvements across conversions, average sales per view and SEO performance metrics — across both paid and organic channels simultaneously. The Bloomreach direct integration partnership enables customers of both platforms to deploy Lily’s attribute enrichment directly into Bloomreach Discovery’s search index with a no-code integration and no taxonomy mapping requirement.
Scored Categories
Pricing
| Option | Price | Details |
|---|---|---|
| Lily Max — 30-day trial | Free | 500 products from your catalogue; enrichment + controlled A/B test; demonstrates commercial impact before full deployment commitment |
| Full deployment | Enterprise custom | All pricing custom-quoted; scales with catalogue size, channel mix and scope of enrichment required; contact lily.ai for commercial terms |
| Bloomreach integration | Included for joint customers | No-code integration; no taxonomy mapping required; enriched attributes automatically populate Bloomreach Discovery search index |
Strengths
- 15,000+ consumer-centric attributes — deepest product vocabulary in Cat 23
- 300+ classification models; 3B+ retail & consumer data points
- 28% Google Shopping revenue lift, 21.4% Meta ROAS lift — rigorously validated
- 10x more attributes per product — transformative for catalogue richness
- Lily Max: structured product data for AI commerce surfaces (ChatGPT, Gemini, Google AI Overviews)
- Trend vocabulary continuously updated by internal retail & style experts
- Bloomreach direct integration (no-code, no taxonomy mapping)
- Enterprise client validation at Tapestry, Macy’s, J.Crew, Abercrombie & Fitch level
Weaknesses
- No published pricing — enterprise custom only; inaccessible to SMB and mid-market
- No self-serve option beyond the 30-day Lily Max trial
- Deepest attribute vocabulary in fashion/apparel/beauty — lighter coverage for other categories
- Implementation requires integration with existing PIM/ecommerce platform — not plug-and-play
- 8–9 figure revenue uplift claims lack published sample sizes or confidence intervals
- G2 review volume modest — enterprise tools often have limited public review data
Verdict: 8.2 / 10 — The Definitive Enterprise Product Attribute Intelligence Platform for AI Commerce
Lily AI’s 8.2 reflects a platform whose core proposition — bridging merchant-speak and consumer-speak through AI-enriched product attributes — is both commercially validated and increasingly strategically essential as shopping discovery fragments across AI surfaces that require structured, machine-readable product intelligence to function. The commercial evidence is unusually robust for Cat 23: 28% Google Shopping revenue lift and 21.4% Meta ROAS lift are validated through matched-spend controlled experiments, not self-reported estimates. The client roster (Tapestry, Macy’s, J.Crew, Abercrombie & Fitch) provides enterprise-grade credibility that few Cat 23 tools match. Lily Max’s positioning as an agentic product intelligence layer for AI commerce surfaces is the right strategic bet for 2026–2028 as consumers increasingly discover products through AI-generated answers rather than traditional search results. The primary constraints are enterprise-only pricing (no SMB path) and category depth that is strongest in fashion, apparel and beauty. For mid-to-large apparel and lifestyle retailers, Lily AI is one of the highest-ROI investments available in the ecommerce AI category.
Frequently Asked Questions
What is the difference between Lily AI and a standard product description AI tool?
Standard AI product description tools — Jasper, Copy.ai, Describely, Hypotenuse AI — generate text from existing product information: you provide a title and a few specs, and the AI writes a sentence or paragraph. Lily AI operates at a fundamentally different layer: it does not rewrite descriptions, it enriches the underlying attribute data that drives search, filtering, recommendations and AI discovery. Where a copywriting AI produces a polished product description, Lily AI produces a structured data layer of 15,000+ consumer-centric attributes per product — occasion tags, trend references, synonym mapping, facet attributes for search and filtering, schema-validated fields for AI shopping surfaces — that populates every downstream channel that consumes your product data simultaneously. A retailer using both would use Lily AI to enrich the product’s attribute and metadata layer, then use a description AI to write the visible product copy informed by those enriched attributes. The performance impact of Lily AI is also measured differently: not by the quality of the text output, but by measurable lifts in Google Ads conversion, Google Shopping revenue, Meta ROAS, onsite search conversion and AI discovery visibility — tracked through controlled A/B experiments against baseline product feeds.
What is Lily Max and how does it differ from Lily AI’s core platform?
Lily Max is the 2026 flagship product evolution from Lily AI’s core catalogue enrichment platform into an agentic product intelligence engine specifically designed for AI commerce surfaces. Where Lily AI’s core platform enriches product attributes for traditional channels (onsite search, Google Shopping, Meta ads, SEO), Lily Max extends that enrichment to the structured, machine-readable product payloads that AI shopping systems require: Google AI Overviews, Meta’s AI shopping integrations, ChatGPT product recommendations, Gemini shopping surfaces, and LLM-based discovery platforms. The practical difference is that Lily Max doesn’t just improve how human shoppers find your products — it improves how AI systems understand, evaluate and recommend your products when generating AI-driven answers to shopping queries. Lily Max also operates as a proper agentic system: goal-based AI agents enrich your catalogue, launch controlled A/B tests across paid and onsite channels, measure the commercial impact of each enrichment, and iterate — producing a continuous product intelligence optimisation loop rather than a one-time enrichment event. The 30-day trial on 500 products is a Lily Max feature specifically designed to generate measurable commercial evidence within a single trial period before full catalogue deployment is committed.
Which verticals does Lily AI serve best?
Lily AI’s attribute vocabulary and classification models are deepest in fashion, apparel, footwear, accessories, beauty, home and lifestyle retail — the verticals where consumer search language is most heavily trend-driven, occasion-specific, and subjective, and where the gap between how merchants tag products and how consumers search for them is largest. A coat described internally as “mid-length wool outer layer” might be searched for as “old money winter coat,” “quiet luxury puffer,” or “workwear wool coat” — the kind of terminology gap that Lily’s retail-specific training data captures more completely than general-purpose AI enrichment tools. For grocery, electronics, industrial, or highly technical product categories, Lily AI’s value proposition is less differentiated because those categories rely less on subjective consumer language and more on specifications, compatibility and SKU-level precision — which general-purpose PIMs and content tools handle adequately. Retailers considering Lily AI outside fashion/apparel/beauty/home should request a trial specifically on their product category to validate attribute depth before committing to enterprise licensing. The Bloomreach partnership is particularly valuable for fashion and lifestyle retailers already using Bloomreach Discovery, as the direct integration delivers Lily’s enriched attributes into Bloomreach’s search index with no additional technical work.