Buy Catalog if you are a consumer brand whose problem is getting found in ChatGPT and Gemini; buy Anglera if your problem is the depth and correctness of the catalog itself, inside a system of record you already own.
Both claim to enrich product data. This page is about where that claim stops.
Catalog is built to make a merchant's products legible to AI shopping surfaces — ingesting the existing catalog, structuring the attributes an agent weighs, and syncing price, availability and variants out to ChatGPT, Gemini, Claude, Google Merchant Center and Amazon — so the honest comparison with Anglera isn't distribution, it's attribute ownership: who is accountable six months on, when a field like last_width or elemental_dose_mg still carries a confident value that quietly stopped being true.
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 Catalog leaves on your desk.
Mines web pages, images, reviews; no supplier PDFs
PDFs, spec tables, drawings, manuals, images, sites
Enriches into fixed 86-field schema; no field discovery
Proposes fields your schema never had
Normalizes specs and variants; no versioned pick lists
Normalizes and governs allowed values, versioned
Classifies products; maps to Google, ACP, UCP formats
Auto-classifies; channel and marketplace mapping
Per-field last_seen_at freshness; no source citations
Every value cites its source doc and page
Agent-optimized output; no B2B/B2C persona variants
B2B specifier and B2C shopper enriched differently
Reddit, Pinterest, Google reviews feed enrichment; query tracking
Reviews, search, competitor rails, social — fed back
Generates FAQs and use cases; structures existing copy
Original copy per persona and channel
Analyzes and serves image URLs; no image generation
Generates studio-grade imagery for photoless SKUs
Background re-enrichment; continuous price, stock, variant sync
Re-enriches on its own after go-live
AI readiness score, 90+ checks, competitor benchmarking
Scored against your standards; nothing publishes below bar
Publishes parallel AI storefront; no write-back endpoints
Writes back to PIM, ERP, warehouse, commerce
Public REST API; UCP/MCP distribution; no webhooks documented
API, webhooks, and MCP servers
Automated pipeline; brand reviews enrichments before publish
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.
"wide 2e trail" and "2e width" run all week and land on a results page where not one shoe states a width — everything is listed by US size only.
Width is a real variant axis for running shoes, but it lives in a sizing image and a marketing paragraph, so neither a filter nor an agent can read it.
last_width (per variant, not per product)B (narrow) | D (standard) | 2E (wide) | 4E (extra wide)The COA reads "magnesium bisglycinate 1000 mg, providing 200 mg elemental magnesium." The storefront title reads "Magnesium 1000mg."
Compound weight and elemental dose are both true numbers that mean different things. Any pass that reads the title fills dose with the larger one, and an agent comparing it against a competitor's 200 mg product reaches the wrong answer.
elemental_dose_mg, kept separate from compound_dose_mg and mineral_formmineral_form: bisglycinate | citrate | oxide | malate | l-threonate | taurate"Will third-party 64mm flat burrs drop into the pre-revision chassis, or only the current one?" — a long thread, no agreement, and the manufacturer never answers.
Compatibility is asked at the level of burr diameter, geometry and chassis revision. The PDP publishes "64mm burrs" and a photo, which reads as complete and answers none of it.
burr_diameter_mm, burr_geometry, compatible_chassis_revisions[]burr_geometry: flat | conical. Profile carried separately: unimodal | bimodal | multi-purposeEnrichment for AI surfaces raises a governance question that distribution doesn't settle on its own: an agent quoting your dose, your width or your compatibility is repeating an attribute a human wrote once. Catalog's own material describes enriched output carrying source provenance, and validation for required-field completeness, unit consistency and impossible combinations — real machinery, operating on the values as written. Anglera works alongside your PIM and whatever distribution layer you run. We take named accountability for a defined attribute set, per category: vocabulary agreed up front (2E, not "wide"; elemental_dose_mg, not "1000mg"), a cited source per value, and a re-check cadence tied to how fast that attribute actually moves. A supplier revision lands as a diff a category manager approves, not a silent overwrite.
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 Catalog stops.
How Schema Foundry works
Catalog is a San Francisco startup (founded 2025, $3M pre-seed led by Acrew Capital with WndrCo and Hustle Fund) building product data infrastructure for agentic commerce. It ingests a brand's catalog from Shopify, WooCommerce, BigCommerce, Adobe Commerce and others — plus reviews and shopper signals from Reddit, Pinterest and Google — then normalizes and enriches products into 86+ machine-readable fields. It distributes that data to AI shopping surfaces (ChatGPT via ACP, Gemini via UCP, Claude, Perplexity, Amazon Rufus, Google Merchant Center) and publishes a parallel "AI storefront" on a brand subdomain. A separate developer API sells product extraction from any URL.
Pricing: Split. The developer API publishes per-call rates: Crawl $0.2/100 listings; Extract $2/100 (Starter), $10/100 (Scale, adds AI enrichment and Reddit insights), $100/100 (Pro, adds image/review analysis, FAQs, deep research). API keys issued by emailing the founders. The brand platform has no public pricing page (/pricing 404s); it is demo-booked, with a free AI readiness audit as entry.
Catalog websiteConsumer brands whose top priority is AI-channel visibility — ACP/UCP distribution, an agent-readable storefront, referral tracking — plus developers wanting per-call extraction from any URL.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Catalog's public documentation on July 20, 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