JSON-LD for AI visibility

JSON-LD structured data gives AI models an unambiguous, machine-readable version of a page's facts, instead of requiring them to infer meaning from prose and HTML layout. CrawlPod checks structured-data coverage and validity as part of its AI Readiness Audit and flags what's missing or invalid.

Why structured data matters for AI citation

When a page states a fact only in prose — a price mentioned mid- paragraph, an author name in a byline styled with CSS rather than marked up semantically — an AI model has to infer that fact from layout and language, which is error-prone and inconsistent across page templates. JSON-LD structured data states the same facts explicitly, in a standardized, machine-readable format, so an AI system doesn't have to guess what a piece of text means or where it applies. That directness is a meaningful factor in whether a model treats a page as reliable enough to cite.

Key schema types

Schema type	What it's for
`Organization`	Identifies who runs the site — name, logo, contact, and social profiles, as one unambiguous entity.
`Product`	Structured facts about a product — name, price, availability — instead of forcing inference from a product page's prose.
`FAQPage`	Marks up question-and-answer content directly, the shape AI answer engines quote most readily.
`Article`	Identifies headline, author, and publish date for blog posts and news-style content.
`Offer`	Structured pricing and availability, often nested inside Product — see below for how CrawlPod's Dynamic AI Promo Codes feature extends this.

Offeris worth calling out specifically: on the Business tier, CrawlPod's Dynamic AI Promo Codes feature generates and maintains Offer markup for active promotions, so AI systems have a structured, current record of your pricing and discounts rather than a prose description that can go stale.

How CrawlPod checks and enhances JSON-LD

The AI Readiness Audit includes structured-data coverage and validity as one of its 9 checks. It looks at which of your pages carry JSON-LD at all, whether the markup present is syntactically valid and matches schema.org's expected shape for its declared type, and whether common required fields are missing. Pages that fail this check are flagged with the specific problem — missing schema entirely, invalid JSON, or an incomplete type — rather than a generic pass/fail, so you know exactly what to fix.

Example FAQPage JSON-LD

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Do you ship internationally?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes, we ship to over 30 countries. Rates and delivery times are calculated at checkout."
      }
    },
    {
      "@type": "Question",
      "name": "What is your return policy?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Returns are accepted within 30 days of delivery, provided the item is unused and in its original packaging."
      }
    }
  ]
}

See the AI Readiness Audit for the full list of 9 checks, and llms.txt setup guide for a complementary, non-schema way to hand AI models a structured summary of your site.