Lily AI
Contact SalesAgentic product intelligence engine that enriches catalogs for AI commerce surfaces.
By Tanmay Verma, Founder · Last verified 05 Jul 2026
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Added 5d ago
75/100Safe BetIn short
Lily AI — Agentic product intelligence engine that enriches catalogs for AI commerce surfaces. Best for Performance marketing teams at retail brands optimizing Google Shopping and Meta ads, Enterprise retailers with large product catalogs needing AI-ready product data, Agencies managing multi-channel e-commerce campaigns for multiple clients. Contact Sales pricing.
Is Lily AI actually worth it?
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Editorial Verdict
Best for
Performance marketing teams at retail brands optimizing Google Shopping and Meta adsEnterprise retailers with large product catalogs needing AI-ready product dataAgencies managing multi-channel e-commerce campaigns for multiple clientsBrands preparing for AI-driven shopping surfaces like ChatGPT and Gemini
Not ideal for
Small businesses with very limited product catalogs (under 500 products)Teams without existing product feed infrastructure or catalog systemUse cases requiring real-time personalization beyond product enrichmentBudget-constrained teams needing transparent, upfront pricing
If you manage significant ad spend and need to prove ROI from feed optimization, Lily Max offers measurable, tested lift. Opaque pricing and enterprise focus make it a non-starter for smaller brands or those without existing feed infrastructure. Alternatives like Feedonomics or ChannelAdvisor may offer more transparent pricing and broader channel support.
Skip Lily AI if Skip Lily AI if you don't already have a product feed or you need transparent, upfront pricing without a sales call.
Compare with: Lily AI vs Channable, Lily AI vs Hostinger Reach, Lily AI vs Semrush One
Last verified: July 2026
What's new in Lily AI
Checked 4 days agoAcross the latest 5 updates: 5 news mentions.
NewsBlog·17 days agoNewest
The most expensive thing in retail right now
CEO Purva Gupta previews a CommerceNext session arguing brands overinvest in future revenue while neglecting current revenue surfaces.
NewsBlog·23 days ago
Google says conversational attributes are optional. History says otherwise.
Argues Google's conversational attributes for AI shopping feeds will become mandatory, driving need for feed enrichment.
NewsBlog·30 days ago
It always turns out to be a feed
Observers that OpenAI's shift to feed-based model mirrors Google Merchant Center, reinforcing feeds as foundation for shopping AI.
NewsBlog·Jun 6
Google may have just created a billion-dollar optimization market
New Google AI Performance Insights makes AI visibility measurable, unlocking budget for feed optimization.
NewsBlog·May 28
Automation commoditized everything except your feed
Describes Jevons paradox in paid search: automation lowered barriers but raised costs, with product feed remaining key differentiator.
What independent users actually report about Lily AI
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
15 mentions across 1 source (Lemmy).
50% positive50% critical
Recurring strengths
- +AI-driven feed enrichment for Google Shopping and Meta Advantage+ compatibility.
- +Controlled A/B testing lets teams validate feed changes before rolling out.
- +AI Performance Insights tie enrichment directly to revenue lift metrics.
- +Goal-based AI agents automate product data optimization across paid channels.
- +Conversational attribute extraction prepares products for LLM-powered shopping assistants.
Recurring frustrations
- −Virtually no real user reviews or community discussions to validate claims.
- −Pricing is not transparent — contact-only model raises cost concerns.
- −Listed integrations as N/A, limiting plug-and-play with existing tools.
- −No free tier or trial mentioned, risking buyer's remorse.
- −Performance claims are vendor-supplied, not independently verified.
Patterns worth knowing
Lack of genuine user feedback — all scraped posts are off-topic or unrelated to the tool.
Seen on Lemmy
Strong feature set for enterprise feed optimization and AI-surface readiness.
Seen on Lemmy
Opaque pricing and integration information create hesitation for potential buyers.
Seen on Lemmy
Learning curve
beginnerProductive in ~A few hours to days
Hidden costs people mention
- • No public pricing — may require annual contracts or setup fees
- • Possible overage charges for high-volume catalogs
Viability Score
75/100
Safe BetHow likely is Lily AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
70
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Product catalog enrichment with AI-generated attributes
- Google Merchant Center feed optimization
- Meta Advantage+ product feed integration
- AI Performance Insights for search term gap analysis
- Controlled A/B testing for feed changes
- Agent-ready payload generation for LLM and shopping agents
- Onsite search revenue lift via enriched attributes
- Conversational attribute extraction for AI shopping surfaces
- Goal-based AI agents for continuous enrichment
- Free 30-day trial on 500 products
- Enrichment scoring and gap analysis
- Matched-spend A/B testing with holdout groups
- Google AI Performance Insights integration
- Schema-validated product payload generation
- Multi-surface support (Google, Meta, AI discovery, onsite)
About Lily AI
Contact SalesIntermediateAPI availableWeb · API
Lily AI offers Lily Max, an agentic product intelligence engine that enriches e-commerce catalogs with structured, machine-readable attributes. Designed for performance marketing teams at brands, agencies, and enterprise retailers, it optimizes product visibility across Google Shopping, Meta Advantage+, AI discovery platforms (like ChatGPT and Gemini), and onsite search. Lily Max uses goal-based AI agents to audit, enrich, and test product data, feeding into ad platforms and LLM-powered shopping assistants. Key features include AI Performance Insights for search term gap analysis, controlled A/B testing for feed changes, and agent-ready payload generation. Unlike generic feed tools, Lily Max ties enrichment to measurable lift, with reported results like +28% revenue on Google Shopping and +21.4% ROAS lift on Meta. Pricing is opaque and tailored to catalog size, with a free 30-day trial available for 500 products.
Behind the Verdict
Lily Max is a specialized tool for e-commerce teams that already have a product feed and want to squeeze more performance from ad platforms and AI shopping surfaces. Its strength is in the rigor of its A/B testing and measurement, which helps justify spend to stakeholders. The AI agents continuously enrich product attributes with search-optimized terms and schema, making products more discoverable by both algorithms and LLMs. However, the lack of public pricing is a barrier for smaller brands, and the tool doesn't manage campaigns or ads directly. It's best used alongside existing feed and ad management tools. Recent blog content positions Lily as a thought leader in the emerging 'agentic commerce' space, which may be valuable for early adopters.
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Real-world workflow fit
Concrete scenarios for the personas Lily AI actually fits — and what changes day-one when you adopt it.
Performance marketing manager at a mid-size apparel brand
Google Shopping performance is flat; you suspect product titles are missing key search terms. You connect your Google Merchant Center feed to Lily Max, run the AI Performance Insights to identify high-volume search terms your products lack, apply enrichment via goal-based agents, and launch a matched-spend A/B test. After two weeks, you see a +28% revenue lift in the test group.
Outcome: You prove the ROI of feed enrichment and roll out the winning attributes across your entire catalog.
Agency lead managing Meta Advantage+ campaigns for a footwear retailer
Meta's automated ad matching is underperforming. You use Lily Max to enrich product attributes with detailed descriptors (material, fit, occasion). You then run an A/B test with a holdout group. The enriched feed drives a +21.4% ROAS lift, and you present the results to the client.
Outcome: Client increases ad spend, and you secure a long-term contract for feed management services.
E-commerce director at a beauty brand preparing for AI shopping surfaces
You've read that ChatGPT and Gemini are recommending products based on structured data. You use Lily Max to generate agent-ready payloads with schema-validated attributes, and your brand becomes the #1 AI-recommended makeup choice in major shopping experiences.
Outcome: Your brand gains organic visibility on AI platforms without additional ad spend.
Use Cases
- Enrich product titles and descriptions with high-demand search terms to improve Google Shopping performance
- Run controlled A/B tests to measure the revenue lift from feed changes before full rollout
- Generate agent-ready product payloads for LLM-based shopping assistants like ChatGPT and Gemini
- Optimize Meta Advantage+ campaigns by enriching product attributes for better ad matching
- Audit existing product data for AI readability gaps and automatically fill missing attributes
- Improve onsite search conversion rates by adding granular, schema-validated product attributes
- Prepare product catalog for AI-powered shopping surfaces and agentic commerce
Models Under the Hood
proprietary (Lily AI agents)LLM shopping assistants (ChatGPT, Gemini, Claude)
as of 2026-07-05
Limitations
- Pricing is not publicly listed and requires a sales call.
- The platform focuses exclusively on product feed enrichment and does not offer ad management or campaign execution.
- Depends on merchants having at least a basic product feed to start.
- Advanced features like agentic commerce support may be gated to enterprise plans.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
—
Contact sales for a quote
Effective monthly
—
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Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Lily AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
Contact for pricing
Ideal for
Growing brands with moderate catalog size (e.g., 500-5,000 products) starting with AI retail media on one or two channels.
What this tier adds
Entry-level tier tailored to smaller catalogs; includes core enrichment and gap analysis but may lack advanced A/B testing and multi-channel support.
Growth
Contact for pricing
Ideal for
Multi-channel brands (e.g., Google Shopping + Meta) with larger catalogs and higher ad spend who need scaling enrichment and A/B testing.
What this tier adds
Adds matched-spend A/B testing and multi-channel enrichment; pricing scales with catalog size and ad spend.
Enterprise
Custom
Ideal for
Large retailers and marketplaces with millions of SKUs needing custom integration, agentic commerce support, and dedicated account management.
What this tier adds
Unlocks custom pricing, dedicated support, and advanced features like agent-ready payloads for LLMs and AI discovery platforms.
Integrations
Where the pricing makes sense
The company stage and team size where Lily AI's pricing actually pencils out — and where peers do it cheaper.
Lily AI is built for brands with substantial ad spend who can negotiate custom pricing. It's expensive relative to self-serve feed tools like DataFeedWatch, but the A/B testing and measurement may justify the cost for enterprise teams. For smaller budgets, consider Feedonomics or free Google Merchant Center tools.
Setup time & first value
How long it actually takes to get something useful out of Lily AI — broken out by persona, not the marketing-page minute.
For brands with an existing Google Merchant Center feed, you can start the free 30-day trial in minutes by connecting the feed. The initial enrichment and gap analysis runs within a few hours. Setting up the first A/B test may take a day. Full integration across multiple channels may take a week.
Switching to or from Lily AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From a basic feed tool (e.g., Google Merchant Center native feed): Import your existing GMC feed directly; Lily Max enriches it without disrupting current feeds.
- →From a traditional feed management platform (e.g., DataFeedWatch, Feedonomics): Export your feed as a CSV or via API; Lily Max can consume it and layer on enrichment.
- →From manual product data management: Use the free trial on 500 products to assess gaps before committing.
Migrating out
- ↗To a full feed management platform (e.g., Feedonomics, ChannelAdvisor): Export enriched attributes as a feed; many platforms accept custom attributes.
- ↗To an in-house solution: Download enriched product data via Lily Max's API or CSV export.
- ↗To a competitor (e.g., Salsify, Akeneo): Enriched attributes can be mapped to PIM schemas.
Resources & Guides
Frequently Asked Questions
Tools that pair well with Lily AI
Common stack mates teams adopt alongside Lily AI, with the specific reason each pairing earns its keep.
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