Buy Harmonya if you are CPG or grocery and want a ready-made, review-derived attribute universe plus demand insights; Anglera if you need new attributes discovered for your schema, generated imagery, and write-back to your system of record.
Both claim to enrich product data. This page is about where that claim stops.
Harmonya is built as an attribute-and-insight layer over a category — the market's language about products, read across retailers and normalized at the UPC level — so the comparison isn't the quality of that first pass, it's who owns the record afterward: the field a retailer added on Tuesday, the spec that exists only on a supplier's PDF, and the resolution rule someone has to author once and re-apply every season.
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 Harmonya leaves on your desk.
Mines listings, pack/label copy, manufacturer specs, images, feeds
PDFs, spec tables, drawings, manuals, images, sites
Surfaces emerging shopper tags; not proposed as new schema fields
Proposes fields your schema never had
Core pitch: harmonizes conflicting retailer schemas into normalized values
Normalizes and governs allowed values, versioned
Category attribution and schema mapping; no channel category mapping
Auto-classifies; channel and marketplace mapping
Every field carries confidence score and lineage to source page
Every value cites its source doc and page
Shopper-led throughout; no B2B-versus-B2C persona variants found
B2B specifier and B2C shopper enriched differently
Attributes derived from 100M+ reviews, listings, demand themes
Reviews, search, competitor rails, social — fed back
No titles, descriptions, or bullet generation found
Original copy per persona and channel
Reads product images; no image generation found
Generates studio-grade imagery for photoless SKUs
Continuously updated as products launch and retailer formats change
Re-enriches on its own after go-live
Per-field confidence scores; no catalog health trending found
Scored against your standards; nothing publishes below bar
Feeds PIM, data lake, Snowflake; no named connectors
Writes back to PIM, ERP, warehouse, commerce
API-based delivery layer; no public docs, webhooks, or MCP
API, webhooks, and MCP servers
Models auto-attribute; your team integrates and owns catalog
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 separate reviewers over six weeks write some version of "looks great, doesn't heat on my induction range" — one adds "the magnet won't stick to the bottom."
The market is loud about the symptom and quiet about the field. Cooktop compatibility isn't an opinion shoppers report; it follows from the base disc material on the supplier's spec sheet, which was never mapped to a field. The listing says "works on all stovetops" in marketing copy and carries nothing in the structured record.
cooktop_compatibility (multi-select), derived from base_material and induction_disc_presentGas | Electric coil | Ceramic glass | Induction | Halogen — multi-select, at least one required; Induction permitted only where base_material is ferromagnetic or induction_disc_present = true"grain free kitten pate" and "kitten wet food no gravy" climb month over month; the result set mixes pâté with shreds-in-gravy and chunks-in-broth, because texture exists only inside the title string, spelled pate, pâté, paté or "smooth loaf" depending on the vendor.
Shoppers are filtering on a facet the catalog doesn't have. Texture is the buying decision in kitten wet food and it lives in free text, so it can't be faceted, can't be merchandised, and can't be checked when a new vendor loads 200 SKUs next quarter.
food_texture_form (single-select, required when format = wet)Pâté | Minced | Shreds in gravy | Chunks in broth | Stew | Mousse — normalises pate, paté, smooth loaf, loaf → Pâté; flaked, shredded → Shreds in gravyThe line sheet says "medium-firm." The spec PDF says 5.5. The ERP export carries Comfort Level 3 of 5. All three describe the same king SKU, and the retailer's schema wants one integer on a 1–10 scale plus a shelf label.
Three internally consistent answers and no single one. Nothing in the source data is wrong and the record still can't publish. The disagreement lives entirely inside the vendor's own documents — line sheet, spec PDF, ERP export — and none of it ever reached a shopper or a listing. The resolution rule has to be authored once, applied across the vendor's whole line, and re-applied when they refresh the collection.
firmness_rating (integer 1–10, required) with derived firmness_label1–3 Plush | 4–6 Medium | 7–8 Firm | 9–10 Extra firm — vendor 1–5 comfort scales map by published crosswalk; "medium-firm" alone is insufficient to set the integer and routes to human reviewHarmonya's raw material is the market's language about products. It uses proprietary large language models to generate attributes from titles, descriptions, ingredients and consumer reviews, resolves identity across UPCs, ASINs and retailer IDs, and feeds the result into a PIM, data lake, dashboards or AI. Its materials say models predict and fill each attribute with a confidence score and source lineage. That is a first pass, and a first pass is where the practice starts. A confidence score is an input to a decision, not the decision: someone still has to rule whether a borderline "gluten free" ships to a retailer that treats the claim as a legal assertion, or holds for human review. Meanwhile the record moves. A repack changes net content but not the copy. A retailer adds a required field mid-quarter. Fill rate is a snapshot; publishable is a standing condition someone holds.
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 Harmonya stops.
How Schema Foundry works
Harmonya is an AI product-intelligence platform for enterprise CPG brands and grocery retailers. It mines retail listings, pack and label copy, manufacturer specs, ingredients, images, and syndicated feeds to attribute products at the UPC level, publishing the result as a standard Harmonya catalog or a custom catalog mapped to a customer's attribute schema. Alongside attribution it sells Demand Intelligence, Consumer Intelligence (built on 100M+ consumer reviews), Nutrition Profile, and a conversational Insights Agent. It is backed by dunnhumby Ventures and W23 Global, and cites six of the top ten global CPG brands as customers.
Pricing: No public pricing. Harmonya publishes no plans, rate card, or free trial; pricing is enterprise and quoted by their sales team based on categories, coverage, and modules. Third-party listings (Datarade, alternative-vendor pages) confirm pricing is undisclosed and custom. Expect an enterprise data-subscription shape rather than per-SKU or self-serve, but treat any specific figure as unverified.
Harmonya websiteEnterprise CPG brands and grocery retailers who want a bought, review-derived attribute universe at UPC level with source lineage, plus demand and consumer intelligence on the same dataset.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Harmonya'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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