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Product Coherence

Product data that
AI engines understand.

Answer engines read your product pages and structured data. Citely audits your catalog, spots inconsistencies and exposes clean schema.org/Product markup — so AI describes your products accurately. The foundation of any citation.

app.citely.app/catalogue/lina-espresso
Catalog / Coffee /

Lina Espresso Machine

Re-audit Publish schema

Lina Espresso Machine

SKU LINA-ESP-15 · Coffee collection
78
AI completeness
3 to fix
TitleLina Espresso Machine — 15 bar, 1.8 L tankOK
DescriptionBarista-quality espresso at home, steam wand…Short · 42 words
BrandMissing
Price€489Feed: €499
AttributesPressure, Capacity, Weight…Incomplete · 3/7
Identifier (GTIN)Missing
Images4 visualsOK
Structured dataschema.org/ProductMissing

Trusted by Shopify brands

NorthpeakHEARTH & COVeridianbriar.SaisonMERIDIAN
The problem

What AI understands about your product.

An engine doesn't guess. Poor or inconsistent data → vague or wrong answer. Structured data → accurate answer that cites you.

Raw data
title: "Lina"
brand:
price: €489 (feed: €499)
schema: missing
attrs: 3/7
AI answer
“I don't have enough reliable information about this product to recommend it.”
With Citely
name: "Lina Espresso Machine"
brand: "Atlas"
price: €489 (consistent)
schema: Product ✓
attrs: 7/7
AI answer
“The Lina Espresso Machine by Atlas (€489, 15 bar, 1.8 L tank) is a solid choice for home espresso.”
Audit

Every page, scrutinized.

Citely reads your catalog and scores each product: fields present, format, richness. You know exactly what to fix, in priority order.

  • AI completeness score per page
  • Missing fields and format flagged
  • Automatic prioritization
audit · Lina Espresso Machine
Title15 bar, 1.8 L tankOK
BrandMissing
Price€489Feed: €499
Attributes3 of 7Incomplete
SchemaProductMissing
Structured data

A schema.org/Product, generated for you.

From your normalized data, Citely produces the structured markup engines expect — price, brand, identifiers, attributes. Ready to expose.

  • Valid schema.org/Product JSON-LD
  • Offers, brand, GTIN, attributes
product.jsonld valid
{ "@context": "https://schema.org", "@type": "Product", "name": "Lina Espresso Machine", "brand": "Atlas", "gtin13": "3401579000128", "offers": { "@type": "Offer", "price": 489.00, "priceCurrency": "EUR", "availability": "InStock" }}
What we check

Six checks, on every page.

The signals engines read to understand — and trust — your products.

Titles

Clear, descriptive, with key specs — not just a name.

Descriptions

Rich enough to answer shoppers' questions.

Attributes & specs

Material, capacity, dimensions, compatibility — complete.

Price & availability

Consistent across the page, feed and variants.

Identifiers

GTIN, MPN and brand — to remove any ambiguity.

Structured data

Valid schema.org/Product, generated and kept up to date.

Catalog view

Your whole catalog, at a glance.

AI completeness product by product, so you can tackle the pages holding back your visibility.

app.citely.app/catalogue

Catalog

412 products · 86% AI-readyFix first
ProductCompletenessStructuredIssuesStatus
Lina Espresso Machine78%3to fix
Runner Pro 294%0ready
Radiance C Serum61%5to fix
Desk Lift Pro88%1ready
Cast Iron Pot 4 L72%2to fix
Aria Earbuds54%6to fix
1–6 of 412 productsPreviousNext
Inconsistencies

Contradictions, caught before the AI.

Prices that differ between page and feed, attributes that conflict across variants, missing brand: Citely detects and surfaces them, ranked by severity.

  • Page ↔ feed ↔ variants gaps
  • Sorted by severity
inconsistencies · 412 products
Price mismatch — Lina Espresso MachinePage €489 · product feed €499
Missing brand — 38 productsEmpty “brand” field, required by schema.org
GTIN missing — 64 productsProduct identifier not set
Description too short — 21 productsFewer than 50 words
How it works

From your catalog to schema, continuously.

Connected to Shopify, updated on every product change.

1

Read the catalog

Products, variants, metafields and collections, read straight from Shopify.

2

Audit & normalize

Missing fields, inconsistencies and formats detected, then harmonized.

3

Structured data

Clean schema.org/Product, exposed to engines and kept up to date.

What changes

Product data AI understands.

By fixing missing data and inconsistencies, your products go from “not found” to accurately described by engines.

schema.orgProduct, Offer, Brandmarkup generated for you
8 fieldschecked per pagetitle, price, GTIN, attributes…
Continuous syncon every changeyour data stays up to date

Frequently asked questions

It's standard markup (schema.org/Product) that describes your product in a format machines read unambiguously: name, brand, price, identifier, attributes. Engines rely on it to understand — and cite — your products.

No. We read your catalog, audit it and expose structured data. Content fixes are suggested to you — you keep control of your Shopify pages.

Most often from a gap between the price shown on the page, the one in the product feed, and the variants. We compare all three and surface the gaps.

No. Clean, structured data is the necessary condition for an engine to understand and be able to cite you — not a guarantee. That's what the Citations part measures.

Yes. Catalog reading happens via Shopify, and the structured markup is exposed on a classic theme as well as a Hydrogen storefront.

Give AI data it can trust.

Connect Shopify: your catalog audit starts at the first crawl.

clean data helps AI understand — it does not guarantee a citation