Both, if AI-shopping visibility is the goal. Wildcard tracks ChatGPT and Gemini rankings and enriches from product photos; Anglera goes deeper on the data model — supplier docs, governed vocabulary, provenance. Overlap is narrow.
Both claim to enrich product data. This page is about where that claim stops.
Wildcard's enrichment is aimed at winning the AI answer; this comparison turns on what happens to those fields after the first pass — when the supplier reissues the spec sheet, a SKU forks into a variant, and a marketplace adds a facet nobody had a field for — because product data is a practice, not a project.
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 Wildcard leaves on your desk.
Product photos and identifiers; no supplier PDFs or manuals
PDFs, spec tables, drawings, manuals, images, sites
Surfaces competitor attribute gaps; not governed field proposals
Proposes fields your schema never had
No documented value normalization or versioned pick lists
Normalizes and governs allowed values, versioned
Generates collection pages; channel category mapping undocumented
Auto-classifies; channel and marketplace mapping
No documented source citations on enriched values
Every value cites its source doc and page
Brand-voice tuning; no B2B/B2C persona variants documented
B2B specifier and B2C shopper enriched differently
Reads AI-assistant queries and competitor listings; feeds enrichment
Reviews, search, competitor rails, social — fed back
Brand-voice descriptions, blogs, collection pages, FAQs
Original copy per persona and channel
Extracts attributes from photos; no image generation
Generates studio-grade imagery for photoless SKUs
Continuous visibility monitoring; auto re-enrichment claimed, undetailed
Re-enriches on its own after go-live
Tracks AI visibility rankings; not data-completeness scoring
Scored against your standards; nothing publishes below bar
Pushes to Shopify metafields, SFCC, Akeneo, Salsify
Writes back to PIM, ERP, warehouse, commerce
API for custom platforms; no webhooks or catalog MCP
API, webhooks, and MCP servers
Agent-run enrichment; human review workflow undocumented
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.
Three separate questions in a month asking whether the jogging stroller fits in a Golf hatchback boot with the rear seats up. The thread ends with another shopper saying "just measure it yourself, mine barely went in."
Folded dimensions are on the supplier's drawing and nowhere in the catalog. The copy says "compact fold," which no filter and no assistant can act on.
folded_dimensions_l_w_h_cmCentimetres to one decimal, always L x W x H, measured wheels-on with the handlebar collapsed — the state the buyer is actually loading."induction" returns four SKUs out of a sixty-piece cookware range. The carbon-steel skillets and the enamelled cast-iron Dutch ovens are all magnetic-base and none of them say so anywhere machine-readable.
Compatibility sits mid-paragraph in the description on some SKUs and is absent on others, so the facet cannot see it and neither can a shopping answer.
cooktop_compatibility (multi-select)gas | electric coil | ceramic radiant | induction | halogen | open flame — derived from base material and base-plate construction, not from marketing copy.A trail runner with heel_stack 32mm and forefoot_stack 28mm populated falls out of the low-drop filter entirely, while the near-identical competitor SKU sits inside it.
A derivable field nobody owns. Both inputs are present and the arithmetic is trivial, but nothing recomputes the drop when the shoe is reissued on a new midsole.
heel_to_toe_drop_mmInteger millimetres, heel_stack minus forefoot_stack, measured at men's US 9 sample size, rounded to the nearest whole mm.Wildcard is explicit about a guardrail we'd defend too: enriched fields stay tied to approved product facts and source evidence, and unsupported claims remain gaps instead of becoming catalog data. The catalog question is what happens to that gap list next month. Suppliers reissue spec sheets. A SKU forks into a 240V variant. A marketplace adds a facet — heel-to-toe drop, kcal per cup — that no schema had a field for. Each event reopens a gap the first pass correctly left open, and none of them announce themselves. Wildcard's stated scoreboard is what ranks, plus every order tied back to the AI surface and the work that earned it. That is a real measurement, and the one its buyer is hired to move. Anglera keeps the other one: fill rate against a category schema, held per SKU, per release. Find the evidence, fill the field, show the source.
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 Wildcard stops.
How Schema Foundry works
Wildcard (YC W25) sells AEO/GEO for e-commerce and retail brands. It monitors where products surface across ChatGPT Shopping, Google AI Overviews, Gemini, and Amazon Rufus, tracks competitor rankings and the queries they win, then uses AI agents to close gaps — enriching data and generating brand-voice descriptions, collection pages, blogs, and FAQs. Catalog enrichment works primarily from product photos and universal product identifiers, extracting material, colorway, fit, style, and dimensions. Structured data pushes into Shopify metafields, BigCommerce, Magento, WooCommerce, SFCC, Akeneo, and Salsify; they also implement ACP and UCP instant checkout.
Pricing: Not publicly disclosed. The site states tiered plans based on number of products monitored and platforms covered, from a Startup tier to Enterprise with custom requirements, behind a "contact us / book a demo" CTA. No dollar figures, seat counts, or SKU thresholds are published, and the /pricing URL 404s as of July 2026. ACP/UCP instant checkout appears to be scoped separately.
Wildcard websiteDTC and Shopify brands whose top priority is getting cited in ChatGPT Shopping and Gemini — especially apparel or vintage catalogs where the product photo is the only reliable data source.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Wildcard'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.
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