Structured data can be expressed inline as microdata, woven directly into HTML tags with itemprop and itemscope attributes, or as a separate JSON-LD block placed in the page head, holding the same facts in clean, isolated JSON. Microdata worked well for early search-engine parsers, but embedding it inline means it is fragile: a template change, a CMS update, or a stray tag can silently corrupt the markup, and parsers must untangle it from surrounding presentational HTML. JSON-LD avoids that entirely by keeping structured facts in one self-contained script block, independent of the visual markup around it, which is why it has become the format search engines and generative models alike prefer to ingest. For a business whose goal is being described accurately by ChatGPT or Gemini, the practical difference is real: JSON-LD injections covering organization identity, services, location, and reviews give a model a clean, unambiguous source of truth, while scattered or malformed microdata often gets skipped or misread entirely.
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Microdata is still technically valid and parsed by some systems, but JSON-LD is now the widely preferred format because it is easier to validate, less prone to breaking during template changes, and simpler for crawlers and models to isolate cleanly.
Organization, LocalBusiness, Service, FAQPage, and Review schema types tend to carry the most weight, since they give a model direct, structured answers to the exact questions buyers ask rather than requiring inference from prose.
Structured data testing tools can confirm syntax validity, but confirming actual ingestion requires checking server logs for crawler requests to the page and monitoring whether AI-generated answers reflect the facts the markup contains.
Proof & data
Most AI-visibility tools only watch — they report where you are absent and stop there. AI HALO does the work that changes the answer, then re-scans to prove it.
Measured live across ChatGPT · Claude · Gemini · Meta AI · Grok · DeepSeek — we ask the models your buyers’ real questions, before and after.
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