Keep inriver as the system of record — it governs, syndicates, and scores the digital shelf. Add Anglera for the enrichment inriver leaves to your team: attributes your schema lacks, cited values, persona-specific depth.
inriver and Anglera solve different halves of the problem — this page is about the seam between them.
With a PIM like inriver the question was never who holds the attribute — inriver is built to hold it, validate it, and syndicate it — the question is where the work lands when the value a buyer is shopping on has never been written down by anyone, and who does that work again next quarter when the question changes.
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 inriver leaves on your desk.
AI extraction from PDF, Word, Excel, PowerPoint onboarding
PDFs, spec tables, drawings, manuals, images, sites
Maps sources into existing model; no new attribute proposals
Proposes fields your schema never had
Value lists, glossaries, intake validation; team curates values
Normalizes and governs allowed values, versioned
AI categorization plus channel and marketplace attribute mapping
Auto-classifies; channel and marketplace mapping
Governed audit history; no per-value source document citation
Every value cites its source doc and page
Channel and audience-tailored copy; no persona-specific attribute sets
B2B specifier and B2C shopper enriched differently
Evaluate tracks competitor listings, share of search; routes to workflows
Reviews, search, competitor rails, social — fed back
Inspire agents generate descriptions, SEO keywords, social posts
Original copy per persona and channel
DAM storage and AI auto-tagging; no image generation
Generates studio-grade imagery for photoless SKUs
Health checks trigger projects; humans execute the re-enrichment
Re-enriches on its own after go-live
Completeness scores, health checks, digital shelf tracking over time
Scored against your standards; nothing publishes below bar
Is the system of record; syndicates to channels, ERP/PLM
Writes back to PIM, ERP, warehouse, commerce
REST API, MCP server since 2025, event listeners
API, webhooks, and MCP servers
Software with human-in-the-loop review; 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 home-improvement retailer's appliance template asks for cutout depth on built-in wall ovens. The feed clears GTIN and carton dimensions, and cutout depth comes through carrying the same number as overall depth — because that was the only depth anyone had.
Rough-opening depth and product depth are different measurements, and only one of them is in the installation manual. Nothing in the catalog distinguishes them, so a well-formed number in the wrong field clears every rule written against the field that exists.
Cabinet Cutout Depth (minimum)Millimetres, integer, measured from finished wall to face frame; held separately from overall product depthThe shell spec on an outerwear program reads "100% recycled polyester, 75D, bluesign approved" in a footnote on page four of the mill's line sheet. It lands in the catalog as free text: "100% rec poly".
Recycled content is a filter on more than one retailer and a claim someone has to substantiate. It arrived as prose about the fabric, not as a number about the garment, and the catalog has no per-component field to hold it.
Recycled Content by Component (%)Integer percent by weight, one value per component: shell · lining · insulation · trimThe rival string trimmers in the category carry a battery-platform value in the left-hand filter and surface when a shopper narrows to the 40V tools they already own. Yours sits in the unfiltered tail — the platform name is in the third paragraph of your marketing copy.
The trimmer is on the platform, and the charger SKU proves it. The fact lives in prose, so the facet has nothing to read and the SKU never enters the comparison set.
Battery PlatformControlled list of platform names with nominal voltage (18V · 20V MAX · 40V · 56V · 80V); one value per SKU, reconciled against the compatible charger and battery SKUsinriver is built to be the system of record: model and store product data with a flexible schema, validate it, and syndicate approved content outward — prepared and approved once, then adapted per channel. Its own framing is that the PIM orchestrates the end-to-end product content lifecycle, and Content Onboarding sits at the boundary before data lands, so information is structured and validated before it enters the PIM. All of that governs data that exists. The layer above it is where catalogs drift. Any schema is a snapshot of what suppliers were sending when it was drawn, and every rule since is measured against that drawing. A field nobody thought to create isn't flagged as missing; it's invisible. Meanwhile a retailer adds a filter rail, a mill rewrites a datasheet footnote, a new battery class ships. Fill rate reads high because it counts the fields you have. Anglera works alongside inriver on the ones buyers now shop on.
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 inriver stops.
How Schema Foundry works
inriver is a SaaS Product Information Management (PIM) platform that helps brands, manufacturers, distributors, and retailers consolidate, govern, enrich, and distribute product content across channels. It bundles PIM, built-in syndication, and digital shelf analytics into one composable platform serving 1,600+ global brands.
Pricing: Subscription-based; Core, Professional, and Enterprise tiers priced by users, modules, and data volume. Specific figures are not publicly listed — custom quotes only.
inriver websiteBrands and distributors needing one governed home for product data across channels, with built-in syndication, competitor and share-of-search tracking, and a REST API and MCP server.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against inriver'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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