Keep Equadis as the PIM and GS1-certified data pool that validates your records and syndicates them to every retailer and marketplace. Add Anglera to complete those records from supplier documents, cite each value, and write the results back into Equadis.
Equadis and Anglera solve different halves of the problem — this page is about the seam between them.
Equadis and Anglera work on the same record at different moments: Equadis checks that it is complete and compliant before it leaves for each retailer, and Anglera is the standing practice that fills it from the source documents and keeps it current as those documents change.
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 Equadis leaves on your desk.
DocExtract reads artworks, packaging, PDFs and images
PDFs, spec tables, drawings, manuals, images, sites
Customizable data model; team decides which attributes to add
Proposes fields your schema never had
GS1 and ETIM standards; recipient rules enforced on publish
Normalizes and governs allowed values, versioned
GDSN and ETIM certified; per-recipient formats in syndication
Auto-classifies; channel and marketplace mapping
Grounded in PIM data; per-value citation not documented
Every value cites its source doc and page
ACE tailors messages to target audiences and channels
B2B specifier and B2C shopper enriched differently
DSA tracks reviews and search visibility; gaps return to PIM
Reviews, search, competitor rails, social — fed back
ACE generates GEO-ready copy, translates into 20+ languages
Original copy per persona and channel
Image links via associated DAM; generation not documented
Generates studio-grade imagery for photoless SKUs
Closed loop: shelf gaps flow back to PIM for correction
Re-enriches on its own after go-live
Validation engine, quality dashboards, recipient rule checks
Scored against your standards; nothing publishes below bar
System of record; syndicates via GDSN and 950+ connectors
Writes back to PIM, ERP, warehouse, commerce
API connectors referenced; public API docs and MCP not documented
API, webhooks, and MCP servers
Software platform; team validates AI suggestions before publishing
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.
"Bought these crackers because nothing on the listing mentioned sesame. The back of the pack says 'may contain sesame'. My son is allergic, so they went straight in the bin."
The record carries the declared allergens from the ingredient list but has nowhere to put the precautionary statement printed separately on the pack. The listing reads as sesame-safe when the packaging says otherwise.
allergen_status per regulated allergen (contains vs may contain, kept as separate values)The 14 allergens in EU FIC Annex II, each tagged Contains | May contain | Not declared, read from the ingredient list and the precautionary line on the back-of-pack artwork.Queries like "gelatin free gummies" and "vegan vitamin gummies" return the whole gummy supplement range, because the gelling agent only appears inside the ingredient declaration.
Gelling agent is buried in free-text ingredients, so there is nothing to facet or filter on. Shoppers who avoid gelatin for dietary or religious reasons scroll and read labels themselves.
gelling_agent, plus gelatin_source when gelatin is presentPectin | Agar | Modified starch | Carrageenan | Gelatin; gelatin_source: Bovine | Porcine | Fish. Taken from the ingredient declaration on the label artwork, not the marketing copy.A batch of face creams is held back on a required shelf-life-after-opening field. The record has nothing; the jar artwork shows the open-jar symbol marked 12M.
The value exists only as a pictogram on packaging, so the channel's field has nothing to receive and the listings wait while someone reads artwork files by hand.
period_after_opening_months (integer), with best_before_date used instead where the product requires oneInteger months from the open-jar symbol (for example 6, 12, 24), per EU Cosmetics Regulation 1223/2009 Article 19; source of record is the final packaging artwork, not the brief.Equadis's validation engine checks the completeness and compliance of every product sheet before publication, and its syndication layer applies each recipient's rules before sending. Those checks are real. But they measure the record against the data model and recipient requirements as they stand today. A supplier reformulates and the new artwork adds a precautionary allergen line. A retailer starts requiring a field it never asked for. Shoppers begin searching for a property that lives only in the ingredient list. The record still passes, because it is complete against the model the team agreed on. Catalogs drift next to a well-run PIM not because the data is wrong on day one, but because the questions keep changing while the model stays put. Keeping up is weekly work: re-read the reissued artwork, propose the new fields, fill them with a citation, push the values back.
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 Equadis stops.
How Schema Foundry works
Equadis is a Swiss SaaS product data suite for brands and retailers in food and beverage, beauty, health and personal care, and CPG. It combines a PIM, an AI Content Engine (ACE), product data syndication through its own GS1-certified GDSN data pool and 950+ connectors, and digital shelf analytics. More than 600 companies use it across 20+ countries.
Pricing: Annual SaaS subscription with deployment support; priced by SKU count, users, modules and distribution channels. Quote-based, no public tiers.
Equadis websiteFood, beauty, health and CPG brands and retailers, especially in France, Switzerland and Benelux, that must publish through GDSN to large retailers and want PIM, data pool, syndication and digital shelf monitoring from one vendor.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Equadis's public documentation on October 16, 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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