Keep apollon OMN as the PIM, DAM and workflow hub that approves product content and publishes it to shops, marketplaces and print. Add Anglera to fill the attribute values first, read from supplier documents with a citation on each, and written back through OMN's REST API into your existing approval workflow.
apollon and Anglera solve different halves of the problem — this page is about the seam between them.
apollon OMN decides who touches a product record and in what order; Anglera is the standing practice that fills each value from the supplier's own documents and hands it to that workflow with the source attached.
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 apollon leaves on your desk.
Supplier onboarder, ERP imports, image text; PDF extraction not documented
PDFs, spec tables, drawings, manuals, images, sites
Team defines attributes and classifications; no proposals documented
Proposes fields your schema never had
BMEcat, ETIM and ECLASS value mapping; team maintains it
Normalizes and governs allowed values, versioned
Classifications plus marketplace connector; Accelerator automates classification
Auto-classifies; channel and marketplace mapping
Workflow processes documented; per-value source citation not documented
Every value cites its source doc and page
AI Text drafts per channel; persona model not documented
B2B specifier and B2C shopper enriched differently
Review and search signal intake not documented; team-led
Reviews, search, competitor rails, social — fed back
AI Text drafts channel copy from attributes; AI Translate
Original copy per persona and channel
AI retouching and crops; Companion creates variants and scenes
Generates studio-grade imagery for photoless SKUs
Workflows generate task lists; team does the re-enrichment
Re-enriches on its own after go-live
Quality analyses and maturity check for channel readiness
Scored against your standards; nothing publishes below bar
Publishes to shops, marketplaces, print; ERP interfaces
Writes back to PIM, ERP, warehouse, commerce
Documented REST API, n8n and Zapier, MCP server in OMN 7
API, webhooks, and MCP servers
Software platform; your team defines approvals and does the 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.
"Used the 10 mm nylon plugs for a wall cabinet in an aerated concrete wall. Two pulled out within a week. Pack just says 'universal'."
The record carries diameter, length and screw size, but not which base materials the fixing is assessed for. That information sits in the manufacturer's European Technical Assessment, so buyers learn it after the cabinet comes down.
approved_base_materials (per fixing, with the matching load class where the assessment gives one)Normal concrete (cracked / non-cracked) | Solid brick | Perforated brick | Aerated concrete | Solid sand-lime | Plasterboard — taken from the ETA or manufacturer's technical data sheet, not the pack front.Installers search "led panel 600x600 ugr19 4000k" and "panel ugr<19 ra90" and get every 600 mm panel in the range, because glare rating and colour rendering only appear in the photometric PDF.
Office lighting specs are written against glare and colour rendering limits, but neither value is a structured field, so there is nothing to filter on and nothing for a tender spreadsheet to match.
unified_glare_rating plus colour_rendering_indexUGR as the declared upper bound (<16, <19, <22, <25) per the manufacturer's photometric data; CRI as integer Ra (80, 90, 95). Source is the luminaire datasheet or LDT/IES file, not the catalogue headline.A range of EPS and PIR insulation boards is rejected on a required reaction-to-fire field. The record says "flame retardant"; the manufacturer's declaration of performance states the Euroclass.
Fire behaviour exists as a marketing adjective rather than a classified value, so the channel field has nothing to receive and the listings wait while someone opens each declaration by hand.
reaction_to_fire_class (EN 13501-1), with the declaration of performance reference stored alongsideEuroclass A1, A2, B, C, D, E, F with smoke (s1–s3) and droplet (d0–d2) subclasses where declared; source is the declaration of performance, cited by document number and page.apollon's own Digital Product Passport guidance says a passport needs structured, traceable information, and that teams should identify sources, assign responsibilities and define how changes are reviewed. OMN covers the last two well: workflows assign tasks by role, processes are documented, and the maturity check confirms content is ready for its channels. A workflow log is built to record who approved a value and when, not which page of which supplier document it came from. When a manufacturer reissues a declaration of performance with a different fire class, or a datasheet changes a rated load, the approved value stays approved while the source underneath it has moved. Traceability has to live on the value itself, and keeping it current is weekly work: watch for reissued documents, update the affected values with their citations, and send them back through the same OMN approval step.
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 apollon stops.
How Schema Foundry works
apollon is a Pforzheim, Germany software company whose OMN platform combines PIM, DAM, channel management and BPMN-based workflow management on one product data source. It serves retail, mail-order, manufacturing and automotive teams, and publishes to online stores, marketplaces, print catalogues and, with OMN 7, AI assistants via an MCP server.
Pricing: Quote-based. Essential, Professional and Enterprise editions are all priced on request; cost depends on product count, users and modules, with support tiers and add-ons such as marketplace and ERP interfaces billed separately.
apollon websiteEuropean retailers, mail-order houses and manufacturers who want PIM, DAM, print catalogue production and BPMN-modeled approval workflows in one platform, hosted in Frankfurt or on their own infrastructure, with ERP connectors for systems like SAP, Dynamics 365, abas and Xentral.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against apollon'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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