Lily AI
Agentic product intelligence that enriches retail catalogs and proves lift with matched-spend tests on Google Ads, Meta Ads, AI discovery and onsite search.
If your catalog is large, your Google and Meta spend is real, and your CFO wants a defensible number rather than a dashboard, Lily Max is one of the few product-data engines that builds the control group into the plan. The pricing page is honest about why there is no rate card: cost per SKU improves with volume, a structured attribute costs less than a narrative description, and each extra market adds processing. That means the buying step is scoping a quote with your SKU count and use cases plus a single-channel pilot, not a self-serve checkout. Teams with small catalogs, or without a feed manager to enrich, will get further with cheaper feed enrichment plugins. If you mainly want
Verified 6d ago · liveness 63/100 · cite: rightaichoice.com/tools/lily-ai
- Performance marketing teams at retail brands with meaningful Google Shopping, Performance Max and Meta Advantage+ spend
- Enterprise retailers with large, attribute-poor catalogs that need to be legible to AI shopping assistants
- Brands whose products barely surface in AI recommendations and want to fix the data inputs
- Teams that must prove enrichment ROI with matched-spend A/B tests and holdout groups
- Small catalogs where setup overhead and a scoped quote outweigh the measured lift
- Merchants looking for a full ad management platform rather than product data enrichment
- Real-time personalization or dynamic pricing use cases outside catalog enrichment
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Skip Lily Max if you want to change bidding, creatives or budget pacing rather than the product data your feed carries, or if your catalog is small enough that a scoped quote and pilot overhead won't be repaid by measured lift.
Each surface you add is a separate line in your quote: pricing scales with the channels you run and the spend Lily manages, so expanding from Google Ads to Meta, onsite and AI discovery raises the number
Lily AI quotes per engagement instead of publishing a rate card, and the variables are catalog size, use-case complexity and markets — so it sits above cheap feed-enrichment plugins aimed at small catalogs, and alongside other enterprise catalog and product-data platforms that price by scope. It is built for retail brands and agencies managing tens of thousands of SKUs across several markets; if you are under a few thousand SKUs on one channel, the scoping effort is hard to justify.
In short
Lily AI — Agentic product intelligence that enriches retail catalogs and proves lift with matched-spend tests on Google Ads, Meta Ads, AI discovery and onsite search. Best for Performance marketing teams at retail brands with meaningful Google Shopping, Performance Max and Meta Advantage+ spend, Enterprise retailers with large, attribute-poor catalogs that need to be legible to AI shopping assistants, Brands whose products barely surface in AI recommendations and want to fix the data inputs. Contact Sales pricing.
What's new in Lily AI
Checked 6 days agoAcross the latest 5 updates: 5 news mentions.
Agentic commerce: what it is, what changed in 2026, and what a brand actually has to do
Explains agentic commerce, the 2026 shifts driving it, and the catalog fixes brands need to make ahead of Q4.
Does feed optimization increase sales? The anatomy of a controlled test
Walks through matched-spend testing, a 28-day holdout and difference-in-differences analysis as the way to measure whether feed work moves sales.
How AI Shopping Assistants Actually Pick Which Products to Recommend
Describes how AI shopping assistants retrieve products based on structured attributes, and how missing attributes disqualify a listing from consideration.
What actually goes in Google Merchant Center conversational attributes
Provides before-and-after conversational attribute examples for apparel and home, plus guidance on where the attribute values should be sourced from.
Merchant Center suspended for misrepresentation: the checklist, in the order we find them
A step-by-step checklist for resolving Google Merchant Center misrepresentation suspensions and filing the appeal in the right order.
What people actually say about Lily AI — is it worth it?
We scanned public community sources for Lily AI on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Lily AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Goal-based AI agents that enrich, evaluate and republish catalog data continuously
- Overall enrichment scoring (0-100) with per-field and per-dimension gap analysis
- Google Shopping attributes: five core attributes generated per SKU for feed approval and match quality
- Meta catalog ads copy generated and quality-checked before it ships
- Organic SEO and AEO metadata: titles, descriptions, alt text and schema markup
- Conversational commerce attributes for chat and voice shopping assistants
- Item setup and classification: category mapping and structured field population
- PDP consumer product copy written on-brand for the storefront
- Agent-ready, schema-validated payload generation for AI shopping surfaces
- Google AI Performance Insights gap analysis surfacing high-demand search terms per product
- Matched-spend A/B testing with holdout groups to isolate revenue lift
- Difference-in-differences analysis with confidence intervals for finance-ready reporting
- 28-day holdout testing across Google Shopping, Meta and onsite search
- Multi-surface optimization across Google Ads, Meta Ads, AI discovery and onsite search
- Human approval workflow before changes reach any live feed
About Lily AI
Lily AI is the product intelligence company behind Lily Max, an agentic engine that takes your existing product catalog, scores how complete each product record is, and has goal-based AI agents enrich the attributes, titles, descriptions, schema markup and agent-ready payloads that Google, Meta, LLMs and retailer-owned search use to decide what shoppers see. Lily Max optimizes four surfaces: Google Ads (Shopping, Performance Max, Demand Gen), Meta Ads (Advantage+ catalog ads, retargeting, prospecting), AI discovery and agentic commerce, and onsite search. The catalog work is sold a la carte. The pricing page lists six enrichment use cases scoped individually in your quote: Google Shopping attributes (five core attributes per SKU tuned for feed approval), Meta catalog ads copy, SEO/AEO metadata (titles, descriptions, alt text, schema markup), conversational commerce attributes for chat and voice assistants, item setup and classification, and PDP consumer product copy. What separates Lily Max from a feed tool is measurement. Every plan includes catalog ingestion, product-data scoring, agentic enrichment, matched-spend A/B testing with holdout groups, and reporting that isolates lift against a control. Published results from named engagements include +28% revenue on Google Shopping for a large apparel brand, +21.4% ROAS on Meta Advantage+ for a national footwear retailer validated against a Meta Conversion Lift Study, +28.3% onsite revenue for a contemporary luxury brand, and a #1 AI-recommended ranking for a beauty brand in ChatGPT and Gemini. It does not replace your feed manager, catalog system or ad platform. You pick your use cases, SKU count and markets on the pricing page, and Lily AI returns a line-itemized quote rather than a rate card; changes ship only after your team approves them. Named customers include Marks & Spencer, Shiseido, Tapestry and Bombas.
Behind the Verdict
Lily Max is best understood as a data layer that sits between your catalog and every system that distributes it. The agent loop collects catalog and performance signals, enriches the gaps it finds, then republishes — and the pricing page is explicit that all plans include catalog ingestion, product-data scoring, agentic enrichment of your highest-impact gaps, matched-spend A/B testing, and reporting that isolates lift against a control. That last clause is the differentiator. Most catalog tools sell you cleaner attributes and leave attribution to you; Lily AI pairs enrichment with 28-day holdout groups and difference-in-differences analysis. Strengths worth naming. First, the use-case manifest is granular: Google Shopping attributes, Meta catalog ads copy, SEO/AEO metadata, conversational commerce attributes, item setup and classification, and PDP consumer product copy each scoped and priced individually, so you can start narrow. Second, the proof is unusually specific — +28% Google Shopping revenue for a large apparel brand in a matched-spend test against its existing Google Merchant Center feed, +21.4% Meta Advantage+ ROAS for a national footwear retailer cross-validated against a Meta Conversion Lift Study, +28.3% onsite revenue for a contemporary luxury brand, and a #1 AI-recommended makeup ranking for a beauty brand in ChatGPT and Gemini. Third, customer testimony from Marks & Spencer, Shiseido and Bombas is consistent about the same thing: feed quality in Google Merchant Center, not dashboard novelty. Fourth, there is an approval workflow — changes only reach a live feed after your team signs off. Weaknesses. There is no published per-SKU rate, so you cannot budget from the website; the pricing page explains the three variables (catalog size, use-case complexity, markets and regions) and asks you to build a quote. Setup is a project, not a signup: catalog ingestion plus scoping across channels and markets, and the pilot produces the lift evidence that justifies a rollout. The agentic loop is deliberately gated by human approval, which suits brand and legal teams but slows teams that wanted unattended full-catalog rewrites. And nothing here handles bidding, budget pacing or creative strategy. Where it fits: enterprise and upper-mid-market retailers and agencies with attribute-poor catalogs, meaningful Google Shopping or Meta Advantage+ spend, and an appetite for controlled measurement. Where it doesn't: small catalogs where scoping overhead outweighs lift, merchants wanting a full ad management platform, and teams wanting real-time personalization or dynamic pricing.
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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.
Run a free feed audit to score the existing Google Merchant Center feed, pick the Google Shopping attributes use case, then launch a matched-spend A/B test comparing Lily-enriched product data against the current feed over a 28-day holdout window
Outcome: A defensible revenue lift number with a control group — the published apparel result was +28% revenue on Google Shopping
Enrich product attributes and catalog ad copy for Meta placements, quality-check the copy before it ships, and validate the test against a Meta Conversion Lift Study
Outcome: Higher ROAS at near-identical spend — the published footwear result was +21.4% ROAS on Meta Advantage+
Generate agent-ready, schema-validated product payloads and conversational commerce attributes, then review how the catalog is retrieved and recommended in LLM shopping surfaces
Outcome: Catalog becomes resolvable by AI assistants — a top beauty brand reported a #1 AI-recommended makeup ranking in ChatGPT and Gemini
Use Cases
- Enrich product titles and descriptions with high-demand search terms to improve Google Shopping performance
- Run matched-spend A/B tests to measure revenue lift from feed changes before full rollout
- Generate agent-ready product payloads for LLM-based shopping assistants
- 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 with granular, schema-validated product attributes
- Prepare a catalog for AI-powered shopping surfaces and agentic commerce
- Create conversational commerce attributes to power chat and voice shopping assistants
Models Under the Hood
as of 2026-10-04
Limitations
- Lily Max requires a product catalog or feed as input and is scoped to e-commerce retail across Google Ads, Meta Ads, AI discovery and onsite surfaces.
- There is no published per-SKU rate on the site: the pricing page states that cost depends on the use cases you select, how many SKUs you have, and how many markets you run, and turns those into a line-itemized quote.
- All plans include catalog ingestion, product-data scoring, agentic enrichment of your highest-impact gaps, matched-spend A/B testing and reporting that isolates lift against a control; higher tiers add channels, optimization cadence, support and governance.
- The system operates under human approval by default, so changes do not reach a live feed until your team signs off, and the agentic loop runs continuous controlled tests with matched spend and holdout groups.
as of 2026-10-02
Verification history
We have re-verified Lily AI 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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 quotes per engagement instead of publishing a rate card, and the variables are catalog size, use-case complexity and markets — so it sits above cheap feed-enrichment plugins aimed at small catalogs, and alongside other enterprise catalog and product-data platforms that price by scope. It is built for retail brands and agencies managing tens of thousands of SKUs across several markets; if you are under a few thousand SKUs on one channel, the scoping effort is hard to justify.
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.
Starting point is your Google Merchant Center feed: Lily AI asks for a sample so it can return a scoped, line-itemized quote — the pricing page says that takes under two minutes of your time. After that, most teams begin with a scoped pilot on one channel, which by design produces matched-spend lift against a control before you commit to a broader rollout. Expect the pilot to include a 28-day
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.
- →From a manual or spreadsheet-driven attribute workflow: point Lily Max at your existing catalog, use the enrichment scoring and gap analysis to prioritise fields, then approve changes before they publish
- →From an existing feed enrichment plugin: keep your feed manager and ad platforms in place and layer Lily Max on top, since it improves the data those systems already distribute rather than replacing them
- →From a feed management platform: run Lily Max alongside it, using the item setup and classification use case to map categories and populate structured fields for new items
- ↗To an in-house enrichment pipeline: export the schema-validated attribute set Lily Max generated and use it as the labelling spec for your own model
- ↗To a cheaper feed plugin: retain the use-case manifest (Google Shopping attributes, Meta copy, SEO/AEO metadata, conversational attributes) as the checklist for what a lower-cost tool must reproduce
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Lily AI”, and we withheld 6: 6 could not be judged, because “Lily AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Lily AI.
Official links
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Featured Head-to-Head Comparisons
Lily Ai vs Weglot
Lily AI and Weglot serve entirely different e-commerce pain points: Lily Max optimizes product feeds for ad platforms and AI shopping surfaces (with recent news emphasizing feed criticality), while Weglot handles multilingual website translation. If your goal is to boost Google Shopping and Meta ad performance via enriched product data, choose Lily AI. If you need to rapidly translate your e-commerce site into multiple languages with brand consistency and SEO, choose Weglot.
Lily Ai vs Chili Piper
Lily AI and Chili Piper serve completely different missions: Lily AI optimizes e-commerce product feeds for ad platforms and AI shopping surfaces, while Chili Piper automates B2B lead conversion from website traffic. Your choice depends on whether your priority is improving product visibility on Google/Meta (Lily AI) or accelerating sales meetings from inbound visitors (Chili Piper). There is no overlap, so choose based on your GTM challenge.
Lily Ai vs Screenplayiq
Lily AI and ScreenplayIQ serve entirely different audiences — one optimizes e-commerce feeds for measurable ad lift, the other predicts screenplay box office returns. If you're a retail marketer seeking to boost Google Shopping or Meta ad performance with AI enrichment, Lily AI is your tool. If you're a screenwriter or producer needing data-driven script feedback and financial forecasts, ScreenplayIQ fits better. The choice is purely domain-based.
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Frequently Asked Questions
Best-of guides
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