Buy Lily AI if you are a B2C fashion, beauty or home brand chasing ROAS and AI-search visibility from an existing feed; buy Anglera if your catalog runs on supplier documents and your data model itself is the gap.
Both claim to enrich product data. This page is about where that claim stops.
Every tool in this market sells you one act and calls it the whole play. The useful question isn't whether Lily AI is good at its act — it's what happens to the other two.
Mine every spec from every source.
Every value traced to a document you can open. The catalog is only as honest as what it was built from.
Aim the catalog at the buyer who actually buys.
Grounded data still loses if it answers questions nobody asked. Alignment is what turns specs into conversion.
Product data is a practice, not a project.
Markets move, suppliers reissue, buyers change what they ask for. A catalog that is right in March is wrong by August unless something is watching.
Scored against public documentation. Grouped by the three acts — so you can see which ones Lily AI leaves on your desk.
Feeds, PDPs, schema, product images; no supplier documents
PDFs, spec tables, drawings, manuals, images, sites
Applies own 20k-term library; no per-customer field proposals
Proposes fields your schema never had
20k+ term retail taxonomy; synonyms collapsed, human-curated
Normalizes and governs allowed values, versioned
Retail taxonomy classification; maps to Google, Meta, Amazon
Auto-classifies; channel and marketplace mapping
Review queue and lift evidence; no value-level source
Every value cites its source doc and page
Consumer language tuned per surface; B2C retail only
B2B specifier and B2C shopper enriched differently
Shopper searches, clickstream, surface performance feed back into enrichment
Reviews, search, competitor rails, social — fed back
Titles, descriptions, meta tags, Q&A, schema markup
Original copy per persona and channel
Computer vision reads images; outputs alt-text, not images
Generates studio-grade imagery for photoless SKUs
Always-on loop re-enriches and retests; human approves deploys
Re-enriches on its own after go-live
Scores completeness, correctness, compliance; gap detection, holdout lift tests
Scored against your standards; nothing publishes below bar
Publishes to GMC, Shopify, Algolia, Bloomreach, PIM connectors
Writes back to PIM, ERP, warehouse, commerce
No-code connectors; no public API docs or MCP found
API, webhooks, and MCP servers
Software agents enrich; customer sets goals, approves deploys
Anglera owns the work; review is a guardrail
Every enrichment tool on the market takes your schema as a given and fills the blanks in it. That ceiling is invisible: your catalog can hit 100% complete and still be missing the attribute that loses the sale — because completeness is measured against a schema someone drew years ago.
3/4 in0.75"3/4"19mm3/4 inchDN200.75 in (DN20)Six suppliers, six spellings, one physical size. Filters only work once they agree.
BlkblackBLACK MATTEMatte BlkRAL 9005Black — MatteFree text makes a colour filter useless. A governed value makes it a facet.
SS316316 StainlessStainless Steel 316A4 StainlessStainless Steel — 316 / A4Same alloy, four vocabularies, plus a trade name. Buyers search all of them.
“Buyer signals” is the emptiest phrase in this category, so here is the literal thing. Each of these is an observation from a live market, the gap it exposes, and the field that gets created as a result.
“Handle hits the wall when you open it” shows up across the 2- and 3-star reviews on a brass ball valve — and in the return reasons behind it.
Clearance is why the product came back, and no field anywhere describes it.
Handle Clearance (fully open)Millimetres, measured from valve centreline“left hand thread” is searched steadily on your own site and returns zero results — while you stock 240 left-hand-threaded SKUs.
You have the products. You do not have the attribute, so search cannot find them.
Thread DirectionRight-hand (RH) · Left-hand (LH)Contractors comparing compressors argue about noise in decibels months before any RFQ mentions it.
The dB rating is sitting in the supplier PDF. It is not a field, so it is not a filter.
Sound Level (dB)dB(A) at 1 m, integerFour of six competitors let a buyer filter welders by duty cycle. Your category has no such field.
Buyers who filter on duty cycle never see your catalog at all.
Duty Cycle% at rated amperage (e.g. 60% @ 200 A)Page 4 of the datasheet has “Ambient operating range −20 °C to +60 °C” in a table nobody ever mapped.
The data arrived years ago and died in a PDF because no field was waiting for it.
Ambient Operating RangeMin/max °C pairListings bounce for a missing Country of Origin that is printed on the packaging and in the customs paperwork.
A required field for the channel that was never required by the PIM.
Country of OriginISO 3166-1 alpha-2Lily AI sells Lily Max, an agentic product intelligence engine that enriches retail catalogs so search engines, ad platforms and AI shopping agents can understand them. It reads a retailer's existing feed, PDPs, schema and product images, detects gaps, then generates consumer-language attributes, titles, descriptions and schema markup drawn from a 20k+ term retail taxonomy. Enrichments are A/B tested against a holdout and winners publish to Google Merchant Center, Meta, Amazon, onsite search and connected PIM/commerce systems. Founded 2015; named customers include Coach, J.Crew, UGG and Kate Spade.
Pricing: Undisclosed. The pricing page lists three quote-only tiers: Starter, Growth and Enterprise — all "book a demo for a quote". The FAQ states pricing "is based on your catalog size and the surfaces you choose, structured as credits," and is tailored to ad spend. A free 30-day trial on 500 products is offered.
Lily AI websiteConsumer fashion, beauty and home brands with a clean product feed whose goal is measurable Google/Meta ROAS and AI-search visibility, with lift proven by holdout testing.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Lily AI's public documentation on July 14, 2026. Vendors ship quickly — if something here is out of date, tell us and we'll correct it.
Bring one category. We'll run Schema Foundry against it and show you the fields your schema doesn't have yet — on your own SKUs, in 30 minutes.
Book a demo