Keep Viamedici as the multidomain system of record that governs, approves and syndicates your product, supplier and asset data. Add Anglera to fill those records from supplier datasheets and manuals, cite each value, and write it back through EPIM's API.
Viamedici and Anglera solve different halves of the problem — this page is about the seam between them.
Viamedici and Anglera work on different layers of the same record: EPIM decides what a product record is and who may change it, and Anglera is the standing practice that reads the supplier's documents and puts the right value in each field.
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 Viamedici leaves on your desk.
Structured imports and supplier portal; PDF mining not documented
PDFs, spec tables, drawings, manuals, images, sites
Configurable meta-model; team designs new attributes and domains
Proposes fields your schema never had
ETIM, eCl@ss, GS1, validation rules, AI unit conversion
Normalizes and governs allowed values, versioned
Automatic classification into categories; ETIM, eCl@ss, GS1 supported
Auto-classifies; channel and marketplace mapping
Audit trails, attribute versioning; per-value source citation not documented
Every value cites its source doc and page
Copilot assists editors; persona-specific enrichment not documented
B2B specifier and B2C shopper enriched differently
Digital shelf listed; search and review loop not documented
Reviews, search, competitor rails, social — fed back
Copilot assists editors with descriptions; AI translation learns corrections
Original copy per persona and channel
DAM with AI image recognition and tagging; generation not documented
Generates studio-grade imagery for photoless SKUs
Event Hub pushes changes; team drives re-enrichment work
Re-enriches on its own after go-live
AI quality monitoring flags gaps; DQI app listed, undetailed
Scored against your standards; nothing publishes below bar
Real-time syndication to channels; certified bidirectional SAP exchange
Writes back to PIM, ERP, warehouse, commerce
Universal API, Event Hub, and MCP server for agents
API, webhooks, and MCP servers
Software platform; agents on roadmap, team owns enrichment today
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 the 125 mm grinder to run our existing flap discs and wire cups. Spindle is M14, our cups are 5/8-11. Back to the counter for adapters before we could start."
The record carries voltage, wattage, no-load speed and disc diameter, but the spindle thread sits only in the operating manual's technical data table. Buyers matching the tool to accessories they already own can't check fit before ordering.
spindle_thread, plus max_accessory_diameter_mmSpindle thread: M10 | M14 | M16 | 5/8"-11 UNC. Diameter as an integer in mm. Read from the technical data table in the manufacturer's manual, not the packaging copy.Queries like "inductive sensor m12 pnp no flush 4mm" and "m18 npn nc non flush" return the whole proximity-sensor category, because output type and mounting only appear inside part numbers and description text.
Switching output, contact function and mounting style are what an engineer filters on first, and none of them exist as structured attributes. A buyer who knows the exact spec scrolls through hundreds of sensors that can't be wired into the panel.
switching_output, contact_function, mounting_type, rated_sensing_distance_mmOutput: PNP | NPN | push-pull | analog. Contact: NO | NC | NO+NC. Mounting: flush (shielded) | non-flush (unshielded). Sensing distance Sn in mm per IEC 60947-5-2, taken from the datasheet's electrical table.A range of cementitious tile adhesives is rejected on a required adhesive classification field. The record says "flexible, high-performance adhesive for large-format tiles"; the bag and the declaration of performance say C2TE S1.
Classification lives as marketing language rather than as the coded performance class the standard defines, so the channel's required field has nothing to receive and the listings sit unpublished while someone reads declarations of performance by hand.
adhesive_class_en12004, deformability_classEN 12004 type and class: C1 | C2 | D1 | D2 | R1 | R2, with modifiers F (fast-setting), T (reduced slip), E (extended open time). Deformability: S1 | S2 | none. Source of record is the declaration of performance, not the brochure.Viamedici's model is built for rigor. Supplier submissions pass validation rules and four-eyes approval, every attribute keeps its version history, and AI monitoring flags gaps and inconsistencies in the product database. When a field is empty, EPIM's AI can suggest a value based on similar products. That suggestion is a reasonable starting point. For a sensor, a fastener or a tile adhesive, though, the value a buyer relies on is the one printed in that manufacturer's datasheet, and the closest sibling in the catalog can differ in exactly the spec that matters. The governed answer is the value read from the source, with the page it came from attached, so an approver checks a citation instead of re-reading the PDF. The model also keeps moving: a reissued datasheet adds a rating, a channel adds a required field. Keeping records current is weekly work: read the new documents, fill the fields, propose the ones the model lacks, push the values back into EPIM.
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 Viamedici stops.
How Schema Foundry works
Viamedici is a German, privately held software company founded in 1999. Its EPIM/5 platform combines MDM, PIM, DAM and CPQ on one configurable multidomain data model that links product, supplier, material, asset and channel data, with BPMN workflows, ETIM, eCl@ss and GS1 classification, certified SAP integration and real-time syndication to stores, marketplaces, print and partner portals.
Pricing: Not publicly listed. Sold through demo and contact requests; deployable in the cloud, hybrid or on-premise.
Viamedici websiteManufacturers in tools, building materials, electronics, automotive and industrial automation that run SAP, sell in many countries and languages, and want product, supplier, material and asset data on one configurable model with formal approval workflows and DPP support.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Viamedici'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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