Buy Syte for visual search and on-site discovery in fashion, jewelry, or home decor; its deep tagging is real but lands in Syte's own layer. Buy Anglera if the catalog data itself has to be right in your system of record.
Both claim to enrich product data. This page is about where that claim stops.
Syte's deep tags describe what a shopper can see, so the shopper can find the product; this comparison is about the spec fields underneath — the ones a mill's fabric swap or a marketplace's new required attribute changes six weeks later, and that no product photo contains.
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 Syte leaves on your desk.
Product images and text; no supplier PDFs or manuals
PDFs, spec tables, drawings, manuals, images, sites
Applies prebuilt 15k lexicon; does not discover new attributes
Proposes fields your schema never had
Lexicon Manager normalizes tags into controlled, mappable value set
Normalizes and governs allowed values, versioned
Auto-categorizes and maps to your taxonomy; on-site, not marketplaces
Auto-classifies; channel and marketplace mapping
Tags Editor allows review; no per-value source citation found
Every value cites its source doc and page
Personalizes recommendations per shopper, not the enrichment output
B2B specifier and B2C shopper enriched differently
Deep Tag Analytics reports trending attributes to merchandisers
Reviews, search, competitor rails, social — fed back
Generates product descriptions; no bullets or FAQs found
Original copy per persona and channel
Visual search and tagging only; no image generation
Generates studio-grade imagery for photoless SKUs
Auto-tags new SKUs on feed refresh; no autonomous re-enrichment
Re-enriches on its own after go-live
Analytics track tag and search performance, not data completeness
Scored against your standards; nothing publishes below bar
Tags retrievable via API; no PIM connector found
Writes back to PIM, ERP, warehouse, commerce
REST API, webhooks, and SDKs; no MCP server
API, webhooks, and MCP servers
Software; your merchandisers review tags and own rules
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.
"Says washable. It's 22mm of pile and about 6kg dry. It does not fit a 4.5 cu ft home washer, and it won't clear my bedroom door."
Pile height and dry weight sit in the mill's spec PDF, not on the SKU. "Washable" is carrying weight it can't support, and the door-clearance question has no field to answer it with.
pile_height_mm, dry_weight_kg, washable_methodpile_height_mm as an integer in millimetres, never low/medium/plush. washable_method as a governed enum: home_machine | commercial_only | spot_clean_only | not_washable."nickel free hoops", "316L surgical steel", "14k gold filled not plated" repeat weekly, mostly landing on zero results or an unfiltered category grid
Shoppers are searching the metallurgy. The catalog carries "gold-tone" in the title and a single metal facet that mixes finish with base material, so plated brass and gold-filled land in the same bucket.
base_metal, plating_material, plating_thickness_microns, nickel_release_testedbase_metal enum: 316L stainless | sterling silver 925 | brass | titanium | 14k gold-filled | solid 14k. plating_thickness_microns numeric. "Gold-tone" is a finish value and is not permitted in base_metal.The incumbent mill states "Abrasion: 40,000 cycles, EN ISO 12947-2." The replacement mill states "50,000 double rubs (Wyzenbeek, ASTM D4157)." The buying sheet records both as "Heavy Duty."
Two incompatible test methods flattened into one marketing word. Martindale cycles and Wyzenbeek double rubs do not convert, so the contract-grade claim on the PDP can't be defended and the trade channel's spec form has nowhere to put the number.
abrasion_test_method, abrasion_rating_value, durability_gradeabrasion_test_method enum: EN ISO 12947-2 (Martindale) | ASTM D4157 (Wyzenbeek). abrasion_rating_value as an integer in that method's own unit. durability_grade derived from method plus value, never hand-entered.Syte's deep tags are generated to feed discovery. Its own material describes 3,000+ fashion-specific attributes — fabric, fit, style, mood — powering intent-driven search, personalization, and product matching, with merchandisers reviewing AI-generated tags against merchandising and brand guidelines. That is a storefront job, scoped to what the model reads off the product. Six weeks later a mill swaps the shell fabric and the care code changes. A marketplace adds a required field for base metal. Returns data says shoppers read "washable" as "fits a home machine." None of that arrives as a picture. It arrives as a support ticket, a feed rejection, a revised supplier PDF, and it lands on spec fields no discovery surface asked about. Anglera runs that second pass, completing and maintaining those fields in the PIM you already have.
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 Syte stops.
How Schema Foundry works
Syte sells an AI product discovery platform for ecommerce: camera and visual search, Shop the Look, personalized recommendations, and augmented text search. Underneath sits automated "deep tagging," which uses visual AI plus NLP to tag catalog items against a lexicon of 15,000+ attributes tuned for fashion, jewelry, home decor, furniture, DIY, and eyewear. A Tags Editor and Lexicon Manager let merchandisers moderate tags and map Syte's attributes onto their own taxonomy, while Deep Tag Analytics reports trending attributes for assortment and inventory planning. Syte was acquired by Nayax and is now also marketed as Syte by Nayax.
Pricing: Not publicly disclosed. Syte publishes no pricing page and routes buyers to a demo; G2 reviewers note the lack of transparent pricing complicates evaluation. Marketing cites ROI figures (e.g. 423% average ROI, up to 90% lower tagging costs) rather than rates or tiers. API access requires contacting Syte for verified developer credentials.
Syte websiteFashion, jewelry, and home-decor retailers whose goal is camera search, Shop the Look, and visual recommendations on-site, and who treat attribute tagging as fuel for that experience.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Syte'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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