Buy Zoovu if you want guided selling, AI search, and enrichment from one vendor and will adopt their front end. Buy Anglera if you want cited, buyer-ready data written back into the PIM you already run, in about 30 days.
Both claim to enrich product data. This page is about where that claim stops.
Zoovu says its enrichment layer complements a PIM and syncs enriched data back into it, so with a platform like Zoovu the question isn't whether AI can fill a field — it's who owns that value the day the supplier reissues the datasheet and the number on the GDSN feed goes stale.
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 Zoovu leaves on your desk.
Mines PDFs, webpages, reviews, images, spreadsheets; no CAD found
PDFs, spec tables, drawings, manuals, images, sites
Prebuilt 50k-attribute ontology adds fields; not market-discovered proposals
Proposes fields your schema never had
Standardizes units, currencies, naming against its ontology
Normalizes and governs allowed values, versioned
Auto-classifies into 17k-category ontology; channel mapping
Auto-classifies; channel and marketplace mapping
No value-level source citations found in docs
Every value cites its source doc and page
Serves B2B and B2C; persona tailoring sits at recommendation layer
B2B specifier and B2C shopper enriched differently
Reviews and zero-party data feed attribute generation
Reviews, search, competitor rails, social — fed back
Generates SEO product copy, descriptions from specs
Original copy per persona and channel
Computer vision reads images; no product image generation
Generates studio-grade imagery for photoless SKUs
Auto-syncs catalog updates; autonomous re-enrichment not documented
Re-enriches on its own after go-live
AI scores products; no data-quality health dashboard found
Scored against your standards; nothing publishes below bar
Enriched data flows back to PIM, ERP, ecommerce platform
Writes back to PIM, ERP, warehouse, commerce
Public REST platform API; no webhooks or MCP found
API, webhooks, and MCP servers
Platform your team configures; enrichment services also offered
Anglera owns the work; review is a guardrail
“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.
Two- and three-star reviews on a cordless impact wrench keep saying the same thing: "battery doesn't fit my 20V tools, sent it back." The listing says "20V MAX" and nothing else.
Voltage isn't the compatibility question — the battery interface is. Nothing on the record names the platform, so no filter, no feed, and no counter person can answer it.
Battery Platform / InterfaceGoverned list of manufacturer battery platform names, one value per record, with a separate flag for bare tool vs kit"autoclavable 134", "134c sterilizable", and "steam sterilizable handpiece" all return zero results across the dental handpiece range, then the session ends. The spec exists — buried in a PDF marked "see manual for reprocessing."
A regulated, decision-making spec lives in an attachment instead of a field, so it can't be filtered on, syndicated, or handed to a distributor.
Sterilization Method & Max Cycle TemperatureMethod from a governed set (steam autoclave, chemiclave, dry heat, non-sterilizable); temperature in °C at a defined hold timeRev C of an industrial LED high bay datasheet reads "L70 ≥ 60,000 h @ 25°C ambient." Rev B said "50,000 hours." The catalog record still carries the number copied off Rev A: "Lifespan: 50,000 hrs."
Rated life was transcribed once as free text, without the maintenance metric or the test ambient, and never re-checked when the supplier revised it. The number is now both stale and unqualified.
Rated Life (L70) and Test AmbientHours as an integer; lumen maintenance metric from a governed set (L70 / L80 / L90); ambient in °C; datasheet revision recorded as the sourceAn enrichment run is a moment. A catalog is a moving thing. Zoovu's enrichment ingests raw data from PDFs, CSVs, webpages and reviews, standardises currencies, measurements and naming conventions, and turns technical specs into customer-friendly descriptions and structured data that powers search, SEO, filters and personalisation; Zoovu says it syncs that enriched data back into the PIM, ERP or ecommerce platform. That output feeds a discovery surface Zoovu also renders. Records drift underneath it. Someone lands `1,400 ft-lb nut-busting torque` on an impact wrench — reads fine on a PDP, wrong on the line card, where fastening torque is what the buyer specs against. Completing a record means a governed value set, a traceable source behind each value, and a rule for when it gets re-checked. That is the practice Anglera runs alongside your PIM.
Values are normalized into a governed, versioned set of allowed values — so a filter works, and keeps working after the next import.
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.
Filling the fields you defined has an invisible ceiling: a catalog can hit 100% complete and still miss the attribute that loses the sale, because completeness is measured against a schema someone drew years ago. Schema Foundry reads competitor listings, buyer searches, review complaints and your supplier docs, and proposes the fields you never defined — which is where Zoovu stops.
How Schema Foundry works
Zoovu is an AI-powered product experience platform that combines product data enrichment, AI search and merchandising, guided selling configurators, and an AI shopping assistant into one suite. It targets B2B distributors, manufacturers, and B2C retailers who want to unify enrichment and front-end discovery in a single vendor.
Pricing: Quote-based, billed annually. Modular by product (Data Enrichment included in every plan; AI Search, Guided Selling, AI Assistant each priced separately). Scales with traffic/interactions volume. No public rates.
Zoovu websiteTeams rebuilding product discovery (guided selling, AI search, shopping assistant) who want enrichment against a prebuilt 50k-attribute, 17k-category ontology from the same vendor.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Zoovu'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 and your supplier files. In 30 minutes you'll see it enriched — complete, structured, and consistent enough to launch on — plus the attributes your schema didn't have yet.
Book a demo