Keep STEP as your system of record: multidomain governance, ML classification to GS1/ETIM/ECLASS, syndication. Add Anglera for the work inside it — mining supplier PDFs, proposing attributes, citing sources, writing back in ~30 days.
Stibo Systems and Anglera solve different halves of the problem — this page is about the seam between them.
STEP can define what a complete record looks like on the day the model ships; keeping tens of thousands of records complete as rules tighten, standards revise and suppliers send new datasheets is a practice, not a project — so this comparison isn't about which system holds the record, it's about who does the upstream work of finding the values STEP's rules are waiting on.
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 Stibo Systems leaves on your desk.
PDFs, scans, images via intake agent; no CAD
PDFs, spec tables, drawings, manuals, images, sites
Automates attribute mapping; does not propose new fields
Proposes fields your schema never had
Governed LOVs and workflows; stewards do the normalizing
Normalizes and governs allowed values, versioned
ML auto-classification; maps to GS1, ETIM, ECLASS
Auto-classifies; channel and marketplace mapping
Audit trails, lineage, confidence scores; no per-value citations
Every value cites its source doc and page
Persona-tailored descriptions generated from existing attributes only
B2B specifier and B2C shopper enriched differently
Partner optimizes downstream; signals do not drive enrichment
Reviews, search, competitor rails, social — fed back
ProductGen AI generates titles, descriptions, SEO copy, translations
Original copy per persona and channel
DAM stores and analyzes images; generates none
Generates studio-grade imagery for photoless SKUs
Batch runs and workflows; humans trigger re-enrichment
Re-enriches on its own after go-live
Completeness scores, meters, sufficiency dashboards, data profiling
Scored against your standards; nothing publishes below bar
Is the system of record; syndicates to channels
Writes back to PIM, ERP, warehouse, commerce
REST, SOAP, GraphQL APIs; MCP server shipped 2026
API, webhooks, and MCP servers
Software with human-in-the-loop; customer's team owns 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.
A brake pad supplier's file gives the FMSI number and the fitment, but friction material appears only in a footnote on the datasheet PDF: "ceramic formulation, low-metallic content". The submitted attribute field is blank.
The record won't clear a required-field validation for the aftermarket catalog, and the value it needs already exists — just in prose, in a PDF, one layer upstream of the portal. Multiply by 4,000 pads across six suppliers and the onboarding queue stops being a queue and becomes a staffing problem.
friction_material_classCeramic | Semi-metallic | Low-metallic NAO | Non-asbestos organic | Sintered metallic"Bright, well built, but it strobes badly on my 0–10V dimmer. The manual doesn't say what dimmer it wants — I had to email the manufacturer to find out it's DALI only."
Dimming is carried as a yes/no flag or left inside the description, so the protocol never becomes a filterable, governable value. Buyers speccing DALI-2 for a warehouse retrofit can't find the fixture, and the ones who buy it anyway send it back. The spec is in the driver datasheet; it just never made it into the record.
dimming_protocol0–10V | 1–10V | DALI-2 | TRIAC (forward phase) | ELV (reverse phase) | PWM | Non-dimmableRepeated searches for "autoclavable 121C centrifuge tube" and "121 autoclave conical tube" return nothing, while the catalog carries polypropylene tubes whose supplier sheets state "withstands repeated autoclaving at 121°C, 15 psi, 20 min".
Sterilization method lives in long-form copy rather than in the model, so the field buyers actually shop on doesn't exist to search or filter. The demand signal is legible in the query log before it is legible in the schema — and the source values are sitting in supplier documentation the catalog already receives.
sterilization_methodAutoclavable 121°C | Autoclavable 134°C | Gamma-irradiated sterile | Ethylene oxide sterile | Chemically sterilizable | Non-sterileSTEP is built to hold a line. Stibo organizes the platform around functions including Data Modeling, Data Quality and Data Governance, and its PIM adds data quality validation — the layer that decides what a trustworthy record looks like and who is allowed to change it. That is the point of it, and it works. But a rule describes the destination, not the route. When the model starts requiring `friction_material_class` for a TecDoc mapping, or a GDSN revision tightens a net-content format, every SKU that predates the rule stays held open until someone supplies the value. Something has to read it off page four of a supplier PDF. Catalogs rot at that seam. Standards revise, suppliers reissue datasheets, a category manager adds an attribute after a run of returns. Anglera does that upstream work, so the records STEP is holding open can close.
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 Stibo Systems stops.
How Schema Foundry works
Stibo Systems makes STEP, an enterprise Master Data Management (MDM) and PIM platform that centralizes product, customer, supplier, and asset data across domains. It is a Gartner Magic Quadrant Leader for MDM, used by large enterprises to govern and distribute product data at scale.
Pricing: Custom enterprise pricing; subscription-based, scoped by users, data volume, and modules. No public pricing. Implementation typically takes months and often requires a systems integrator.
Stibo Systems websiteLarge enterprises that need one governed system of record across product, supplier, customer and asset data, with ML classification to GS1/ETIM/ECLASS, completeness scoring, and channel syndication.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Stibo Systems'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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