Buy Vizit if you need to know which product image converts and why — it measures imagery but never makes any, so pair it with something that generates the assets and fills the attributes it doesn't touch.
Vizit and Anglera solve different halves of the problem — this page is about the seam between them.
Vizit's axis is the picture — proprietary content scores that predict how a brand's images and video will perform with a given audience, down to rearranging which shot leads the carousel; the practice question sits one layer down, in the attribute record under that image, and it comes back every week the pack changes, the retailer adds a required field, or a shopper needs a spec that only a field can carry.
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 Vizit leaves on your desk.
Reads images for visual cues, not spec documents
PDFs, spec tables, drawings, manuals, images, sites
Scores existing imagery; proposes no new attributes
Proposes fields your schema never had
Visual analytics only; no attribute value normalization
Normalizes and governs allowed values, versioned
Category benchmarks for scoring; no SKU classification
Auto-classifies; channel and marketplace mapping
Explains score drivers via visual cues; no value citations
Every value cites its source doc and page
Audience Lens scores same image per consumer segment
B2B specifier and B2C shopper enriched differently
Behavioral image data and competitor benchmarks feed recommendations
Reviews, search, competitor rails, social — fed back
Imagery only; no titles, descriptions, or bullets
Original copy per persona and channel
Scores and reorders existing images; generates none
Generates studio-grade imagery for photoless SKUs
Always-on monitoring; automated monthly benchmark updates
Re-enriches on its own after go-live
Vizit Score is the core product; tracks over time
Scored against your standards; nothing publishes below bar
Salsify app reorders PDP carousel; no attribute writeback
Writes back to PIM, ERP, warehouse, commerce
Salsify app integration; no public API docs found
API, webhooks, and MCP servers
Software recommends; brand's team creates the content
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.
"The carafe looks identical to the old model in every photo. Mine is glass, not thermal, and the coffee is cold in forty minutes. Nowhere on the page does it say which one you're getting."
Carafe type is visible in the imagery but is not a field, so it can't be filtered, compared, or syndicated — and two variants photograph the same.
carafe_typeGlass / Thermal (vacuum-insulated stainless) / CeramicItem bounces with "Net Content and Net Content UOM required." The front of the pack reads 12 FL OZ; the record carries Size: 12 and a free-text pack description.
The measure exists on the label and in the photo, but the record has a number with no unit — the retailer needs the value and the UOM as separate governed fields.
net_content_uomGS1 UOM codes: fl oz / mL / L / oz / lb / g / kg / ct"fragrance free moisturizer" is a top-20 query and returns 40 results. The claim appears on the tube in the hero image, and in copy as "unscented," "no added fragrance," and "fragrance-free" across three brands in the same category.
A purchase-deciding claim is carried by artwork and by prose in three spellings, so the facet can't be built and the query returns the whole category.
fragrance_statusFragrance-free / Unscented (masking fragrance present) / Lightly fragranced / FragrancedVizit grades pictures: content scores predict how images and video will perform for an audience, Audience Lens explains which visual elements are doing the work, and its Conversion Optimizer app in the Salsify PXM App Center can rearrange the visual content in a product carousel. That output is about the image. The attribute record beneath it is a different artifact, moving on its own clock. Pack art gets refreshed to 14 ct while the record still says 12. A claim lives only in the picture — fragrance-free printed on the tube, a UL mark on the base of the lamp — until someone writes it into a field, where it can be filtered, syndicated, and read by a model. Retailers gate item setup on attribute completeness, and the fields they add each quarter arrive with no image attached. Vizit ranks the photo. Keeping the record true to it is weekly work in a governed vocabulary.
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 Vizit stops.
How Schema Foundry works
Vizit is a Boston-based visual AI platform that predicts how product imagery will perform in ecommerce. Its patented models scan a stated 15,000+ visual cues per image and apply "Audience Lens" scoring — trained on online behavioral data rather than surveys — to predict how a given consumer demographic responds. Products include Vizit Score, PDP Visual Grader, always-on Content Effectiveness Monitoring, and a Conversion Optimizer app for Salsify that automatically reorders PDP carousel imagery. Publicly named customers include L'Oréal, Mars, Kimberly-Clark and Ghirardelli; it raised a $25M Series B led by Industry Ventures in October 2024.
Pricing: Not publicly disclosed. Vizit publishes no pricing page or tiers; all paths route to a demo request. The only public signal is a G2 reviewer noting that cost made it hard to justify as a global rollout across multiple markets, which points to enterprise contracts scaled by market or catalog scope. Treat any figure as unverified until quoted.
Vizit websiteCPG and brand teams with a full creative pipeline already producing imagery, who need audience-specific evidence on which asset converts and how to order the PDP carousel.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Vizit'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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