Keep Catsy as your PIM/DAM and syndication path to Amazon, Wayfair and Shopify; add Anglera to mine supplier PDFs and drawings, propose attributes your schema lacks, and cite every value before writing back.
Catsy and Anglera solve different halves of the problem — this page is about the seam between them.
Catsy holds the golden record and, since late 2025, generates content against it — this comparison is about the research work of finding the value that belongs in the field across 40,000 SKUs, and again every time a supplier reissues a spec sheet or a retailer adds a required enum.
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 Catsy leaves on your desk.
Computer vision on images; CSV/feeds/APIs; no PDF or doc mining found
PDFs, spec tables, drawings, manuals, images, sites
Flags gaps against your existing schema; no new attribute proposals
Proposes fields your schema never had
ETL standardizes formats; validation rules flag inconsistencies for review
Normalizes and governs allowed values, versioned
ML SKU classification plus Google, Amazon, retailer category mapping
Auto-classifies; channel and marketplace mapping
Audit history and version control; no value-level source citations
Every value cites its source doc and page
Segment and region copy variants; no B2B/B2C persona split
B2B specifier and B2C shopper enriched differently
Competitive intelligence and market trends inform AI copy; press-release claims
Reviews, search, competitor rails, social — fed back
Real-time AI descriptions and SEO copy; 95+ market localization
Original copy per persona and channel
DAM stores and resizes assets; no AI image generation
Generates studio-grade imagery for photoless SKUs
Monitors channel performance, suggests updates; no supplier doc re-mining
Re-enriches on its own after go-live
Content scoring dashboards, readiness checks, completeness gap tracking
Scored against your standards; nothing publishes below bar
Syndicates to Shopify, BigCommerce, marketplaces; ERP and API integrations
Writes back to PIM, ERP, warehouse, commerce
REST API, webhooks, and a GA MCP server (2025)
API, webhooks, and MCP servers
Software platform; customer team owns enrichment and approvals
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.
An LED troffer feed bounces back on the required dimming field. The record is not empty — the description says "0-10V dimmable, compatible with most standard drivers" — but the retailer's template wants one value from a fixed list, and free text is not one of them.
The dimming protocol exists in the source content as marketing prose, not as a governed field with a fixed enum. Settling whether it is DALI or TRIAC means opening the driver datasheet.
Dimming Protocol0-10V | 1-10V | TRIAC (forward phase) | ELV (reverse phase) | DALI-2 | PWM | Non-dimmableThree suppliers describe the same hydraulic crimp fitting three ways: one says "1/2 JIC 37°", one says "SAE J514 -08", one prints "3/4-16 UNF flare" in a table footnote. Same part, three strings, and the ERP took whichever arrived first.
End connection and dash size are the two fields a distributor customer actually filters on, and they arrive as prose in PDF footnotes across a supplier base. Until someone normalises them, identical parts do not dedupe or compare.
End Connection Type + Nominal Dash SizeConnection: JIC 37° flare | ORFS | SAE O-ring boss | NPTF | BSPP | Code 61 flange. Size: SAE dash -04 through -32, thread spec carried separately"20a plug on neutral", "PON breaker", and "2 pole bolt-on 20 amp" all return nothing. There are several hundred matching breakers in stock; the mounting style is sitting in the description line of every one of them.
Mounting style decides whether the breaker fits the panel on the truck. It is text a person can read and a filter cannot, so the counter takes the call instead.
Breaker Mounting StylePlug-on | Plug-on neutral (PON) | Bolt-on | DIN rail | Panelboard stabCatsy is built to store, govern and syndicate product content: it validates required fields, image specs and compliance markers before publish, scores completeness, and routes items through custom approval flows. In November 2025 it also announced an AI enrichment engine — description generation, computer-vision extraction of specifications from product images, localization — so it is not a storage-only PIM. The work Anglera does sits next to that. A supplier reissues a spec PDF and the golden record still carries last year's torque figure. A retailer adds a required enum and 12,000 fastener SKUs need an answer that lives in a drawing, not in the existing record or the product photo. Someone has to establish that `coating` is `Zinc CR+3 trivalent` and not hot-dip galvanised, and stand behind it. Anglera does that research and hands Catsy values worth governing. The PIM stores; the practice fills.
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 Catsy stops.
How Schema Foundry works
Catsy is a combined PIM and DAM platform for mid-market brands, manufacturers, and distributors that centralizes product data and digital assets, then syndicates content to retail channels like Amazon, Wayfair, and Shopify. In late 2025 they launched an AI content-generation layer that auto-populates missing attributes and writes product descriptions from existing data.
Pricing: Tiered plans (Starter, mid-tier, Enterprise); specific prices not public — requires demo/quote.
Catsy websiteMid-market brands and distributors that want product data and assets in one system and syndicated out: Catsy covers PIM, DAM, ML categorization, content scoring and channel writeback.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Catsy'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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