Schema Foundry

Your catalog can be
100% complete and still wrong.

Completeness is measured against a schema someone drew years ago. If the field that loses the sale isn't in it, the score can't see the problem — and neither can any tool that takes your schema as a given. Which is all of them.

Schema Foundry is the part of Anglera that questions the schema itself.

Seven machined metal blocks on a dark slate bench: four are old and worn, three have just been cast and still glow molten green.
Four fields you already had. Three that didn't exist until something went looking for them.
Schema Foundry: signals from reviews, search logs, competitor listings and supplier documents reveal attributes missing from your schema; the Foundry discovers, normalizes and governs them, so your schema ends the cycle with more fields than it started with.
One cycle. The schema ends it larger than it started.
Six signals, six fields

Where a missing field announces itself.

A missing attribute is never silent. It shows up as a complaint, a dead search, a competitor's filter, a rejected listing. The signal is always there — the question is whether anything is reading it.

Review signal·Product reviews & returns notes

“Handle hits the wall when you open it” shows up across the 2- and 3-star reviews on a brass ball valve — and in the return reasons behind it.

Clearance is why the product came back, and no field anywhere describes it.

Field createdHandle Clearance (fully open)Millimetres, measured from valve centreline
Search signal·On-site & marketplace search logs

“left hand thread” is searched steadily on your own site and returns zero results — while you stock 240 left-hand-threaded SKUs.

You have the products. You do not have the attribute, so search cannot find them.

Field createdThread DirectionRight-hand (RH) · Left-hand (LH)
Social signal·Trade forums, YouTube teardowns, TikTok

Contractors comparing compressors argue about noise in decibels months before any RFQ mentions it.

The dB rating is sitting in the supplier PDF. It is not a field, so it is not a filter.

Field createdSound Level (dB)dB(A) at 1 m, integer
Competitor signal·Competitor listings & filter rails

Four of six competitors let a buyer filter welders by duty cycle. Your category has no such field.

Buyers who filter on duty cycle never see your catalog at all.

Field createdDuty Cycle% at rated amperage (e.g. 60% @ 200 A)
Supplier signal·Supplier PDFs, spec tables & drawings

Page 4 of the datasheet has “Ambient operating range −20 °C to +60 °C” in a table nobody ever mapped.

The data arrived years ago and died in a PDF because no field was waiting for it.

Field createdAmbient Operating RangeMin/max °C pair
Marketplace signal·Channel rejection logs

Listings bounce for a missing Country of Origin that is printed on the packaging and in the customs paperwork.

A required field for the channel that was never required by the PIM.

Field createdCountry of OriginISO 3166-1 alpha-2

A discovered field is worthless until the values agree.

Finding the attribute is half of it. Six suppliers will send you the same value spelled six ways, and a filter built on free text is a filter that doesn't work. So the Foundry normalizes, then governs: allowed values, versioned, with the synonyms mapped in.

Nominal Size
3/4 in0.75"3/4"19mm3/4 inchDN20
0.75 in (DN20)

Six suppliers, six spellings, one physical size. Filters only work once they agree.

Finish
BlkblackBLACK MATTEMatte BlkRAL 9005
Black — Matte

Free text makes a colour filter useless. A governed value makes it a facet.

Material
SS316316 StainlessStainless Steel 316A4 Stainless
Stainless Steel — 316 / A4

Same alloy, four vocabularies, plus a trade name. Buyers search all of them.

See it on a real category.

The Attribute Schema Library is Schema Foundry's output, published. Every page lists the attributes the category needs — and the ones most catalogs are missing entirely.

Reasonable objections

What is Schema Foundry?

Schema Foundry is the part of Anglera that improves your data model, not just the data inside it. It reads competitor listings, buyer search queries, review complaints, social chatter and your own supplier documents to find attributes your catalog has no field for — then proposes them, normalizes their messy values, and promotes them into governed vocabulary.

How is this different from a PIM completeness score?

A completeness score measures your catalog against the schema you already have. If the schema is missing the attribute that loses the sale, you can score 100% complete and still lose. Schema Foundry questions the schema itself — it is the only measurement that can tell you about a field you never defined.

Doesn't this mean AI inventing fields we don't want?

No. Discovered attributes are proposals, each one attached to the signal that produced it — the competitor filter rail, the search query with no results, the datasheet table. Your team promotes them, and your governance rules decide what becomes canonical. Nothing enters the schema because a model felt like it.

Where do the values come from once a field exists?

From your documents. Anglera mines supplier PDFs, spec tables, drawings, manuals and product images to populate the new attribute, and every value carries its citation — source document, page, and extraction path. Values are extracted, not generated.

What's missing from your schema?

Bring one category. We'll run Schema Foundry against it and show you the fields you don't have — and the signal that found each one. 30 minutes, your own SKUs.

Book a demo

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