Keep Ergonode as the PIM your team works in — workflows, bulk editing, DAM, completeness scoring, channel sync. Add Anglera to fill the attributes: mine supplier PDFs, discover missing fields, cite every value, write it back.
Ergonode and Anglera solve different halves of the problem — this page is about the seam between them.
Ergonode's completeness sets can tell you the PD wattage field on a USB-C hub is empty and restrict the status change until it's filled — this comparison is about who does the filling, and who decides that field should have existed in the first place.
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 Ergonode leaves on your desk.
AI Complete reads uploaded PDFs, images, existing descriptions
PDFs, spec tables, drawings, manuals, images, sites
AI fills attributes you already defined; no field discovery
Proposes fields your schema never had
Select option lists and validation rules curated by your team
Normalizes and governs allowed values, versioned
Rule-based category automation; channel category mapping
Auto-classifies; channel and marketplace mapping
No per-value source citation found on AI-filled fields
Every value cites its source doc and page
Custom prompts and channels; personas configured by you
B2B specifier and B2C shopper enriched differently
No review, search-query, or competitor listing mining
Reviews, search, competitor rails, social — fed back
AI Content Generator writes names, descriptions; AI translations
Original copy per persona and channel
DAM stores images, video, 3D; no image generation
Generates studio-grade imagery for photoless SKUs
Rule-triggered automations rerun; no autonomous re-enrichment
Re-enriches on its own after go-live
Completeness scores in catalog and Kanban views
Scored against your standards; nothing publishes below bar
Syncs to Shopify, Channable, ERP via Apps connectors
Writes back to PIM, ERP, warehouse, commerce
GraphQL and REST API, app event endpoints; no MCP server
API, webhooks, and MCP servers
Software your team operates; no managed service
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.
"Ordered my usual 9, toe box was tight across the forefoot, went up to 9.5 and it's fine. Third pair from this brand that runs narrow." The same note shows up in the return reason field, keyed as "size/fit".
Fit is the subject of most reviews and most returns, but the shoe template carries size and colour. Last width and how the shoe runs against standard sizing exist only in prose, so there is no typed value to facet on, to publish in the spec block, or to check against the supplier sheet — and the return repeats.
forefoot_fit_vs_standard (Select), plus last_width_mm (Numeric, unit mm, measured at UK 8) so the judgement has a number underneath itRuns narrow / True to size / Runs wide — each bound to a measured forefoot width band, not to a reviewer's mood"usb c hub 100w passthrough", "dock 90w charge macbook", "65w pd hub" — a steady stream of queries all landing on a category page with no facet for charging power and results ranked on description text.
Power delivery sits in marketing copy rather than a typed attribute, so there is nothing for a facet or a validation rule to attach to, and each SKU phrases it differently: "100W", "100 watts PD", "charges your laptop while connected".
usb_c_pd_output_w (Numeric, unit W) and usb_c_pd_passthrough (Select), sourced from the spec sheet rather than the marketing lineInteger watts from the manufacturer spec; passthrough as Supported / Not supported / Unspecified — an explicit "Not supported" so an empty cell still means unknown rather than zeroThe supplier PDF reads "CCT 4000K, CRI >90, DALI-2 dimmable". The catalog record reads "cool white, dimmable" in the description and nothing in the attribute grid.
Three fields that specifiers compare on are collapsed into two adjectives sitting in free text. Two suppliers' "cool white" turn out to be 3500K and 5000K, and "dimmable" covers TRIAC and DALI-2, which are not interchangeable on site.
correlated_color_temperature_k (Select), cri_minimum (Numeric), dimming_protocol (Select) — read out of the spec sheet and checked against the ERP lineCCT: 2700K / 3000K / 3500K / 4000K / 5000K / 6500K. Dimming protocol: Non-dimmable / TRIAC / 0-10V / DALI-2 / CasambiA PIM makes missing fields visible and holds a SKU until they're handled. Ergonode does this specifically: completeness is defined as required attributes inside a product template, and status transitions can be restricted on that measure. The math is honest about the template it was handed. Templates are drawn by people, though, and `usb_c_pd_output_w` only exists if someone thought of it the day the hub template was made. If nobody did, every hub reports complete while the question buyers actually type goes unanswered. The work isn't cleaning dirty data — it's proposing fields as the market asks new questions, and re-checking values that were true when a supplier's spec PDF was uploaded and wrong two revisions later. Anglera does that work and writes the results back; Ergonode stays the record and the gate. Someone has to run it continuously. That 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 Ergonode stops.
How Schema Foundry works
Ergonode is a modern open-source/open-SaaS headless PIM platform built for e-commerce and content teams managing large, multilingual product catalogs across multiple channels. It provides workflow automation, bulk editing, DAM, and structured attribute management — but enrichment of the data itself is the team's responsibility.
Pricing: Free tier available; paid plans start around $1,300/month. Tiers: Free, Essential, Professional, Scale, Enterprise. Pricing is public on their website.
Ergonode websiteTeams that want a modern headless PIM they can run themselves — multilingual catalogs, GraphQL/REST, rule-based category automation, and public pricing from free to enterprise.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Ergonode'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