AI Citability Checker (Can ChatGPT verify this?)

If you have ever wondered “is my product AI search ready?” — meaning, can ChatGPT, Perplexity, or a shopping agent actually verify your facts and cite your page — this checker gives you a straight answer. Paste a product URL, raw HTML, or a feed row, and it scores how citable each item is to an AI answer engine, then tells you exactly what is missing.

What it checks

The checker evaluates the signals an AI model uses to decide whether a product is trustworthy enough to name in an answer. It looks at:

• Structured data presence — whether valid Product schema (JSON-LD or microdata) is present, with name, brand, sku/gtin, offers, price, and availability populated.
• Attribute completeness — coverage of the specifications a buyer would ask about (dimensions, material, voltage, compatibility, pack quantity), so the model can match a query to your item.
• Identifier resolvability — presence of a GTIN/UPC, MPN, or brand+model pair that an agent can cross-check against other sources. Unverifiable items rarely get cited.
• Claim verifiability — whether marketing claims (“waterproof,” “OEM-equivalent,” “food-safe”) are backed by a concrete attribute or spec the model can ground against, versus floating adjectives.
• Consistency — agreement between the visible page copy, the structured data, and any provided feed row. Conflicts (price, title, brand) make a model discard the item.
• Crawlability hints — basic checks for whether the content is in the rendered HTML rather than locked behind client-side rendering or blocked paths.

Each signal returns a pass, warning, or fail with a plain-language reason, and the tool rolls them into a single citability score from 0 to 100.

How it works: scoring whether your product is AI search ready

AI answer engines do not “rank” pages the way classic search does. They retrieve candidate facts, then decide which ones are safe to repeat. A model is far likelier to cite a furniture listing that exposes Product schema with a resolvable GTIN and a complete materials/dimensions spec than an industrial fastener page that only has a glossy hero image and a paragraph of prose. The checker mirrors that decision: it parses your input the same way a retrieval system would, extracts the structured and unstructured facts, and tests whether each claim can be independently grounded.

The scoring logic draws on the public Schema.org Product vocabulary and common feed standards (the same attributes used by major product feeds), not a proprietary ruleset, so the results are portable across CPG, MRO, furniture, and industrial distribution catalogs.

For a single SKU this is a spot check. For a catalog, the real question is whether thousands of items clear the bar at once — which is where validating identifiers, normalizing attributes, and writing provenance-tracked structured data back to source becomes a data-layer problem rather than a per-page edit. That is what Claro’s AI search readiness work is built to handle.

Related resources

FAQ

Is my product AI search ready if it already ranks well on Google? +

Not necessarily. Classic SEO rewards links, page authority, and keyword relevance. AI answer engines reward verifiability — structured data, resolvable identifiers, and grounded claims. A furniture page can rank on page one of Google and still be skipped by ChatGPT because its dimensions and material live only in an image. Run the checker to see the gap.

Why would ChatGPT cite a competitor instead of my product? +

Usually because the competitor’s facts are easier to verify. If their MRO listing has a GTIN, complete specs, and consistent structured data, while yours has a vague title and no schema, the model picks the one it can ground. See our guide on why AI tools recommend competitors for the full breakdown.

What is the single biggest factor in citability? +

Resolvable identity. If an AI agent cannot tie your product to a stable identifier (GTIN, UPC, MPN, or a clean brand+model pair) that it can cross-check elsewhere, it treats the item as unverifiable and avoids citing it. Structured data and attribute completeness come close behind.

Does this checker upload my catalog anywhere? +

No. Every check runs entirely in your browser. Nothing you paste or upload is transmitted, stored, or logged, and there is no file-size limit. You can run it on confidential CPG or industrial catalog data safely.

My score is low — what should I fix first? +

Start with the failed signals in this order: add valid Product structured data, populate a resolvable identifier, then fill the missing buyer-facing attributes. The completeness and schema-generation tools linked above turn those fixes into a repeatable, catalog-wide process.