Keep Quable to store, score and syndicate your catalog. Add Anglera for the enrichment it assumes you already did: mining specs from supplier documents, finding attributes your schema lacks, writing it all back into Quable.
Quable and Anglera solve different halves of the problem — this page is about the seam between them.
Quable is built to model, hold, govern and distribute product content; this comparison is about who does the work of getting a defensible value into each of those fields — once at launch, and again every time a supplier, a season or a channel changes what the right value is.
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 Quable leaves on your desk.
CSV, Excel, API and feed imports; no document mining
PDFs, spec tables, drawings, manuals, images, sites
AI fills attributes you defined; no new-field discovery
Proposes fields your schema never had
Holds reference lists and pick lists; team curates values
Normalizes and governs allowed values, versioned
Classification tree and channel mapping, configured by the team
Auto-classifies; channel and marketplace mapping
Records additions, edits, deletions; no source-document citations
Every value cites its source doc and page
Per-channel and per-market content; no persona-tailored enrichment
B2B specifier and B2C shopper enriched differently
Prompt library reflects trends; reads no live signals
Reviews, search, competitor rails, social — fed back
Generates titles, descriptions, SEO copy; 136-language translation
Original copy per persona and channel
Image Studio crops, resizes, auto-tags; no image generation
Generates studio-grade imagery for photoless SKUs
AI enrichment runs on demand; scheduled imports move data
Re-enriches on its own after go-live
Per-channel completeness scoring, real-time quality dashboards
Scored against your standards; nothing publishes below bar
Channels, Apps and connectors push data to commerce platforms
Writes back to PIM, ERP, warehouse, commerce
REST and GraphQL APIs plus webhooks; no MCP server
API, webhooks, and MCP servers
Software platform; customer's team performs the enrichment work
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.
"The pump quit with a fifth of the bottle left — had to unscrew the top and scoop it out" recurs across the 2- and 3-star reviews on two serums, and behind a run of "not as described" returns.
Dispensing mechanism decides how much product a buyer can actually get out, and it lives nowhere but the packaging photo.
Dispenser TypeAirless pump / Dip-tube pump / Dropper / Squeeze tube / Open jar / Spray atomiser"no palm oil", "palm free" and "sunflower oil free" run week after week and return almost nothing — the exclusions are buried in the free-text ingredient statement, which the search index treats as prose.
Buyers filter on what is absent, and absence is only recorded as a sentence someone wrote once.
Free-From Claims (multi-select)Governed claim list: Palm-oil free / Gluten free / Lactose free / Nut free / Soy free / No added sugar — each tied to the ingredient record it was derived fromA womenswear drop bounces on required attributes: sleeve length arrives as "3/4", "three quarter", "3/4 sleeve" and blank across the same style range, and the receiving category enum accepts one of them.
The value exists in the merchandising sheet as prose; the channel wants a coded term, and nobody owns the mapping between the two.
Sleeve LengthSleeveless / Cap / Short / Elbow / Three-quarter / Long / Extra-long — mapped per marketplace to that channel's own enumThe data model gets built — products plus adjacent repositories for materials, colours, ingredients and recipes — catalogue structure is set per channel, workflows organise collaboration across teams, and Quable records every addition, modification and deletion. None of that decides what goes into the field. A field exists for fibre composition and most of the range carries nothing in it. Feeds import from ERP, CRM, Excel files and customised databases, and the same finish arrives spelled three ways; a faithful model stores all three. A supplier reissues a datasheet in March; the stored value stays what it was, and the change history accurately shows nothing changed. Filling those fields, normalising them, and keeping them true as the source documents move is separate work, and it never finishes. That work is Anglera's.
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 Quable stops.
How Schema Foundry works
Quable is a SaaS PIM (Product Information Management) platform used by 300+ brands across fashion, luxury, food, and industrial sectors. It centralizes product data, manages workflows, and distributes content to e-commerce, marketplace, B2B, and D2C channels — with some AI-assisted translation and basic enrichment features built in.
Pricing: Starts at approximately $1,745/month; custom enterprise pricing above that tier.
Quable websiteFashion, luxury, food and industrial brands syndicating one governed catalog to many channels: per-channel completeness scoring, 136-language translation, REST/GraphQL and connectors.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Quable'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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