Buy GroupBy if you're buying its search and discovery engine and want enrichment tuned to feed it; buy Anglera if enrichment has to live in your PIM, cite every value to a supplier document, and follow how buyers search.
Both claim to enrich product data. This page is about where that claim stops.
With an enrichment-AI tool the real question isn't whether the run fills the fields you already model — it's who decides, in month seven, that hinge side needed to be a field at all, when a vendor reformats its spec file and the search log names an attribute nobody has modelled yet.
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 GroupBy (Enrich AI) leaves on your desk.
Mines product text and images; no supplier document mining shown
PDFs, spec tables, drawings, manuals, images, sites
Adds attributes from prebuilt Global Taxonomy library, not autonomous discovery
Proposes fields your schema never had
Global Taxonomy standardizes attribute values; synonyms out of box
Normalizes and governs allowed values, versioned
Core strength: Global Taxonomy classification plus taxonomy creation, management
Auto-classifies; channel and marketplace mapping
Enrich Viewer reviews values; no per-value source citations found
Every value cites its source doc and page
Attribution aligned to shopper search intent; no persona-split output
B2B specifier and B2C shopper enriched differently
Site search queries, null searches, trends refine attribution strategy
Reviews, search, competitor rails, social — fed back
Attribution-focused; description generation not clearly documented
Original copy per persona and channel
Reads images to extract attributes; does not generate images
Generates studio-grade imagery for photoless SKUs
Catalog-as-a-Service maintains enrichment as catalog churns
Re-enriches on its own after go-live
Automated data quality checks; completeness scorecards not documented
Scored against your standards; nothing publishes below bar
PIM/PXM integration cited; primarily feeds GroupBy discovery platform
Writes back to PIM, ERP, warehouse, commerce
Headless REST API and SDKs; no public webhooks or MCP
API, webhooks, and MCP servers
GroupBy curators and ML, plus customer review in Enrich Viewer
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.
"ansi z87.1 anti-fog clear" returns nothing, and so do several near-variants of it — while safety-eyewear SKUs sit in the catalog with "meets ANSI standards" and "fog-resistant lens" buried in marketing copy.
The standard marking and the lens coating exist as prose, not as fields. There is no facet to build, so the query has nothing to match on and the demand stays invisible in fill-rate reporting.
eye_protection_standard_marking + lens_coatingMarkings: Z87, Z87+, D3, D4, D5 (ANSI/ISEA Z87.1). Coating: anti-fog, anti-scratch, anti-fog + anti-scratch, mirror, uncoated. Marketing phrasing ("fog-resistant", "fog-free") maps in; it does not become a value.Separate questions asking whether the door swing can be reversed, and a two-star review whose only complaint is that the reversing hinge kit had to be ordered separately.
Door swing decides whether the unit fits a line layout, and buyers are asking a human because the field doesn't exist. Reviews are pricing a spec the catalog never captured — and the same question repeats across every reach-in SKU on the site.
door_hinge_side + field_reversible + reversing_kit_includedHinge side: left, right, field-reversible. Reversible and kit-included are strict yes/no — "reversible" with the kit sold separately is two values, not one merged one.Within one vendor's own file, the same filter line arrives as "MERV 13" on some rows, "13 MERV" on others, and "MPR 1900" on the rows that came from the retail packaging SKUs.
Three ratings scales and two word orders describing one efficiency level. The facet splits into buckets that each look thin, so filtering on efficiency hides most of the range from the shopper. Nothing is missing; everything is unnormalised — and it re-breaks on the next quarterly file.
filtration_efficiency_mervMERV 1–16 as an integer. MPR and FPR values map to their MERV equivalent at ingest and are retained as source-scale aliases, not as separate facet values. Nominal size parses out to its own field rather than riding along in the description.GroupBy names the objective plainly: Enrich AI is built to "deliver highly relevant search results by enhancing product discoverability and reducing search friction," enriching catalog data against its proprietary Global Taxonomy library. The page also documents taxonomy creation and management, humans-in-the-loop curation, and catalog-as-a-service upkeep for products that have churned. Those are real surfaces; the question is what arrives at them. A vendor re-issues a spec sheet and moves the rating out of the item title into a footnote. A buyer adds forty SKUs whose deciding spec — hinge side, standard marking, coil length — isn't a field yet, anywhere. Zero-result logs name an attribute nobody modelled. None of it surfaces as an error: fill rate stays flat while the facet stops matching how people actually buy. Deciding which fields should exist is standing work, and it comes back every quarter.
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 GroupBy (Enrich AI) stops.
How Schema Foundry works
GroupBy is an AI-first eCommerce search and product discovery platform (powered by Google Cloud Vertex AI) that launched Enrich AI in August 2024 as a catalog enrichment module. It uses generative AI and GroupBy's proprietary Global Taxonomy Library to extract and standardize product attributes from text and images, primarily for B2C and B2B retail and wholesale eCommerce.
GroupBy (Enrich AI) websiteRetail and wholesale eCommerce teams standardizing on GroupBy search who want attributes classified against a maintained Global Taxonomy and refined by their own site-search and null-query data.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against GroupBy (Enrich AI)'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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