Keep Sales Layer to store, validate and syndicate product data; add Anglera to mine supplier PDFs, find the attributes your schema is missing, and write cited, complete records back into it.
Sales Layer and Anglera solve different halves of the problem — this page is about the seam between them.
Against a PIM, "practice, not project" is a question of where the work lands: Sales Layer's Gap Scanner can tell you thread pitch is empty on 12,000 fasteners and hold the answer once it exists — someone still has to go find "M8 × 1.25" this week, and again next week when the supplier ships 400 more SKUs.
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 Sales Layer leaves on your desk.
Spreadsheet, ERP and feed imports; no unstructured document mining
PDFs, spec tables, drawings, manuals, images, sites
Gap Scanner flags empty fields within existing schema only
Proposes fields your schema never had
Natural-language rules validate units; pick lists defined manually
Normalizes and governs allowed values, versioned
Smart Categorizer auto-assigns categories, tags, UNSPSC codes
Auto-classifies; channel and marketplace mapping
No source citations shown on AI-filled values
Every value cites its source doc and page
Serves B2B and B2C; no persona-tailored output variants
B2B specifier and B2C shopper enriched differently
No review, search-query, or competitor signal ingestion
Reviews, search, competitor rails, social — fed back
Generates descriptions and SEO copy across 50+ languages
Original copy per persona and channel
Improve Images enhances resolution; no image generation
Generates studio-grade imagery for photoless SKUs
Agents rerun on schedule or item update; configured actions only
Re-enriches on its own after go-live
Quality Score 0-100, Gap Scanner, quality trend reports
Scored against your standards; nothing publishes below bar
Connectors push to Shopify, Magento, ERP; MCP read/write
Writes back to PIM, ERP, warehouse, commerce
REST API plus hosted OAuth MCP server; webhooks unconfirmed
API, webhooks, and MCP servers
Software platform; customer team configures agents, owns work
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.
A solvent supplier reissues its SDS; section 9 now reads flash point 11.5 °C (closed cup) where the prior revision said "approx. 13 °C". Nothing downstream moves — the value lives in the PDF, and no one has read the new revision.
Flash point decides packaging, placarding and which buyers are even allowed to purchase — and it changes on the supplier's schedule, not yours.
Flash Point (closed cup)Degrees Celsius, one decimal, with the test method recorded alongside — Pensky-Martens or Abel.Zero-result queries in the electrical category cluster around "DIN rail 35mm power supply" and "TS35 mount". The products exist and they do rail-mount. The words don't.
Mounting method is buried in description prose on some SKUs and absent on others. Before it can be a facet, someone has to read the datasheets, settle the value list, and populate 3,000 rows.
Mounting MethodDIN rail (TS35) | DIN rail (TS15) | Panel / surface | Chassis | 19-inch rackA batch of exterior cladding panels bounces from a marketplace category import: "reaction to fire classification required". The datasheets behind those SKUs say "Euroclass B", "B-s1,d0" and "Class B (EN 13501)".
The classification sits in the source documents but was never modelled as an attribute, so every channel that requires it gets a hand-typed value and the three spellings never reconcile.
Reaction to Fire Class (EN 13501-1)A1 | A2-s1,d0 | B-s1,d0 | B-s2,d0 | C-s3,d2 | D-s2,d0 | E | F — full class with smoke and flaming-droplet suffixes, no bare letter accepted.A PIM makes incompleteness visible. Gap Scanner identifies missing fields and errors in product data. Quality Score returns an aggregated score in real time by product, language, channel or taxonomy, flagging duplicate titles, missing attributes, empty categories and incorrect GTINs, with custom rules to catch errors before publishing. That is real work, well done, and it's the honest starting point. But the answer to a flagged gap doesn't live in the PIM. "M8 × 1.25" is on page 3 of a supplier PDF that changed revision last quarter. Someone has to open it, read it, and decide whether it's still the same spec. That isn't a one-time cleanup. A distributor onboards a vendor, a channel adds a required attribute, and the gaps arrive again on the supplier's schedule. Sales Layer stores and syndicates the answer. Finding it, every week, is the practice.
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 Sales Layer stops.
How Schema Foundry works
Sales Layer is a cloud-based Product Information Management (PIM) platform that centralizes product content, supports bulk editing, validation, localization, and digital asset management, and syndicates data to multiple sales channels. It targets marketing and ecommerce teams at mid-market and enterprise manufacturers, distributors, and retailers.
Pricing: Starts at ~$1,000/month; custom quote required for most plans. 30-day free trial available. Tiered plans by team size and feature set (Core → Enterprise).
Sales Layer websiteMarketing and ecommerce teams that need one place to bulk-edit, localize and syndicate product content, with auto-categorization, quality scoring, and connectors into Shopify, Magento and ERP.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Sales Layer'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.
Book a demo