ViSenze if your problem is on-site search and discovery — its tagging exists to feed that. Anglera if the catalog itself is the problem. Many retailers run both: ViSenze fronts the storefront, Anglera keeps the data behind it correct.
Both claim to enrich product data. This page is about where that claim stops.
Product data is a practice, not a project: ViSenze's Smart Tagging reads a product image and generates attributes whose stated job is to make the catalog more searchable and shoppable — the comparison is about the second pass, the attributes that only ever existed on a size chart or a tech pack, and the week a value quietly stops matching the SKU.
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 ViSenze leaves on your desk.
Computer vision on product images; no supplier documents
PDFs, spec tables, drawings, manuals, images, sites
Fills its own fashion taxonomy; proposes no new fields
Proposes fields your schema never had
Fixed vendor attribute set; no free-text value reconciliation documented
Normalizes and governs allowed values, versioned
Auto-classifies into 70+ fashion categories, style-based taxonomies
Auto-classifies; channel and marketplace mapping
No value-level source citations in docs
Every value cites its source doc and page
B2C shopper focus; no persona-specific attribute sets
B2B specifier and B2C shopper enriched differently
Reports zero-result queries, query trends to merchandisers
Reviews, search, competitor rails, social — fed back
Tags and attributes only; no descriptions generated
Original copy per persona and channel
Visual search and tagging; generates no images
Generates studio-grade imagery for photoless SKUs
Tags on catalog ingest; no documented autonomous re-enrichment
Re-enriches on its own after go-live
Flags missing attributes; search analytics, not catalog health
Scored against your standards; nothing publishes below bar
Tag export via BigCommerce, Magento, WooCommerce connectors
Writes back to PIM, ERP, warehouse, commerce
REST catalog and tagging APIs; no webhooks or MCP
API, webhooks, and MCP servers
Software; merchandising team still owns the 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.
"Frames are gorgeous, but they slide down my face all day and the pads sit on my cheeks. I have a low nose bridge — nothing fits me. Wish the listing said whether this is the alternative-fit version."
An image supports shape, colour and material. Bridge geometry decides whether the frame stays on a face, and it lives in the supplier's frame spec — bridge width in millimetres, nose-pad type — not in the photo. Shoppers are doing the fit research in the review section because the field doesn't exist on the record.
bridge_fitstandard bridge | low-bridge / alternative fit | adjustable nose pads | universal fit"washable rug 8x10 no shed" and "machine washable rug pet" recurring week over week; shoppers reach a style landing page, open the filter rail, find no care facet, and leave.
Care method exists only as prose in the description — "easy care", "wipes clean", "shake out regularly", "do not saturate" — in a dozen phrasings across a dozen vendors. Nothing can be faceted, so the intent in the query has nowhere to land.
care_methodmachine washable – cold, gentle | machine washable – warm | spot clean only | professional clean only | vacuum only, no wet cleaningA batch of ankle boots bounced with "shoe_width is required". Width appears nowhere on the record — it lives on the box label and in the vendor's size-chart PDF.
The split isn't image quality, it's provenance. Width is a fitment fact from a size chart: it never reaches the record unless someone opens the PDF, maps the vendor's "wide" to the channel's code, and re-checks it when the vendor revises the last.
shoe_widthB (narrow) | D (medium) | E | EE (wide) | EEEE (extra wide)Smart Tagging uses AI computer vision to read product imagery — ViSenze's own worked example returns heel, brown, leather and buckle for a pair of ankle boots — and ViSenze states the job of those tags plainly: make the catalog more searchable and shoppable, via style-based taxonomies and long-tail terms. That is a discovery brief, and it is met. The second pass is a different brief. The outsole compound, the Goodyear welt that makes a boot resoleable, the lining composition sitting in a supplier's tech pack — none of that is in the frame. A search index is forgiving about a thin record: it ranks oddly, and nobody files a ticket. A channel feed is not forgiving. There the same value is a claim a marketplace validates and a customer returns against. Then the vendor re-issues the tech pack with a new fibre blend, and the record has to move with it — a standing job, not a one-time load.
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 ViSenze stops.
How Schema Foundry works
ViSenze sells an AI search and product discovery platform for retail, marketed as the Discovery Suite. Its modules are Multi-Search (text, image, and multimodal queries), AI Recommendations, Experience Studio, Analytics, and GenAI Smart Tagging, which uses computer vision to auto-apply attributes to product catalogs across a taxonomy of 70+ fashion categories and 225+ attributes. Named customers include Target, Urban Outfitters, Mango, Zalora, Tod's, and Myntra. ViSenze was acquired by Nasdaq-listed Rezolve AI in August 2025 and now operates as a Rezolve company, with its developer docs served under Rezolve's domain.
Pricing: Undisclosed. The pricing page lists three tiers — Light, Pro, Enterprise — differing by enrichment depth (Light: basic fashion attributes; Pro: adds style and occasion; Enterprise: premium enrichment plus fuller analytics). No prices, billing units, or terms are published; every tier routes to "Get a Quote." Third-party directories cite a 30-day trial; ViSenze's own site does not.
ViSenze websiteFashion and apparel retailers whose main gap is on-site search, visual search, and recommendations, and who want image-based tagging tuned to the storefront rather than a system of record.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against ViSenze'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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