Buy EKOM if your problem is systems that disagree: PIM, ERP, and supplier feeds asserting different values someone has to arbitrate and cite. Buy Anglera if the specs your buyers ask for aren't in any of those systems yet.
Both claim to enrich product data. This page is about where that claim stops.
EKOM works inside your existing schema — profiling its columns, correcting them, applying your taxonomy — and this comparison is about the fields that schema does not have yet: deciding one should exist, defining its values, and filling it across a whole category.
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 EKOM AI leaves on your desk.
Mines datasheets, spec docs, supplier feeds, reviews, marketplace specs
PDFs, spec tables, drawings, manuals, images, sites
Corrects within existing schema; no new-attribute proposals
Proposes fields your schema never had
Normalizes to ETIM/GS1 reference libraries; units and synonyms reconciled
Normalizes and governs allowed values, versioned
Auto-classifies plus mapping to 40+ marketplace standards
Auto-classifies; channel and marketplace mapping
Evidence chain cites source doc per value, with confidence scores
Every value cites its source doc and page
Serves B2B and B2C verticals; no persona-tailored output
B2B specifier and B2C shopper enriched differently
Keyword trends, reviews, Q&A feed back into enrichment
Reviews, search, competitor rails, social — fed back
Generates titles, descriptions, SEO fields, FAQs, alt text
Original copy per persona and channel
Writes image alt text only; no image generation
Generates studio-grade imagery for photoless SKUs
Always-on monitoring; re-fits data as marketplace standards drift
Re-enriches on its own after go-live
Scores SKUs against 40+ standards; surfaces gaps and drift
Scored against your standards; nothing publishes below bar
Writes back to Shopify, commerce platforms, marketplaces, EDI partners
Writes back to PIM, ERP, warehouse, commerce
API and EDI distribution; no webhooks or MCP found
API, webhooks, and MCP servers
Recommends fixes; customer team approves, named approver per decision
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.
Four stars on a 30" undermount stainless sink: "Sink is fine. The drain sits dead center, so my disposal ended up fighting the trap arm in a 33\" base — I had to re-plumb. The listing gives me a hundred dimensions and not that one."
Drain placement decides whether the bowl clears an existing disposal and trap arm in a 33" base. It is a dimensioned callout on the manufacturer's drawing, and the sink schema has no field for it — so it is not a column to correct, it is a column to define, then fill across every sink in the category.
drain_positionCenter | Rear Center | Offset Left | Offset Right | DualRecurring zero-result queries: "cri 90 2700k dimmable", "high cri downlight 90+", "cri90 4in canless", "ra90 recessed". Sessions end on the no-results page rather than a refined search.
Color rendering index is printed on every driver label and spec sheet and is a first-cut filter for spec buyers, but it lives as prose inside the description rather than as a field. There is nothing to facet on, so the query dead-ends. Fixing it means agreeing the field exists, agreeing its bands, and backfilling the catalog.
color_rendering_indexCRI 80-89 | CRI 90-94 | CRI 95+ — normalizes "Ra 90", "90+ CRI", ">90 CRI", "High CRI" onto one railTheir left rail carries Backset as a filter — 2-3/8" and 2-3/4" — sitting above Finish. Our rail offers Finish, Handle Style, and Keying; backset shows up only as a line partway down the description text.
Backset decides whether a lockset lands on an existing door's bore, so it is the first cut a replacement buyer makes. It is not a disagreement between our systems — the field is simply not in our taxonomy, so it has to be added and populated across the category before anyone can filter on it.
backset2-3/8 in | 2-3/4 in | Adjustable 2-3/8-2-3/4 inEKOM's platform page is precise about where the work lands. Normalize "profiles every column, validates each record against its siblings, and recommends corrections inside your existing schema." Enrich "applies your taxonomy, units, and channel rules," and infers missing fields from related products. It sits above the stack — PIM, ERP, and feeds stay in place — and every call carries a confidence score, proceeding under governance above your threshold and reaching a person below it. The question we care about sits one level up: who decides a column should exist. When buyers filter entry locksets by backset and your taxonomy has no backset field, there is nothing to profile against or infer from. Adding it is a schema change plus a population pass across the category, and both need an owner.
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 EKOM AI stops.
How Schema Foundry works
EKOM, from Nashville-based AGYL AI, calls itself a resolution layer for product data: it reconciles conflicting product records across a customer's PIM, ERP, supplier feeds, and spec documents into one evidence-cited record, then distributes that record in each channel's required format across Amazon, Walmart, Mirakl, Shopify, BigCommerce, Salesforce Commerce Cloud, and partner EDI and API feeds. It reads existing systems in place with no migration, flags cross-field conflicts with confidence scores for human approval, and serves brands, retailers, and distributors in both consumer and B2B verticals. It also ships a self-serve Shopify app (launched February 2025) that optimizes titles, descriptions, SEO fields, alt text, and FAQs for AI search and buying agents.
Pricing: Enterprise product is sales-led with no public pricing (the CTA is Request a catalog analysis). The Shopify app is free to install with purchased optimization credit packs that never expire, and third-party software listings show earlier plans at 109 and 495 dollars per month.
EKOM AI websiteBrands and retailers whose values already exist but conflict across PIM, ERP, and supplier feeds, and B2C or Shopify sellers needing per-channel distribution across 40+ marketplace standards.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against EKOM 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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