Organization, SoftwareApplication, FAQPage, HowTo, Article, DefinedTermSet and Product schemas. Always JSON-LD, never microdata.
Google can guess. LLMs cannot afford to. When ChatGPT or Perplexity composes an answer, it ranks candidate facts by how confidently they are extracted. Explicit JSON-LD gives the model a 100% confidence signal, while prose-only content forces a probabilistic guess. The result: structured pages dominate citation lists.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "AuraCite",
"url": "https://auracite.de",
"logo": "https://auracite.de/logo.png",
"sameAs": [
"https://github.com/auracite",
"https://www.linkedin.com/company/auracite"
],
"founder": { "@type": "Person", "name": "Mohamad Galaedin" },
"foundingDate": "2025",
"areaServed": "EU"
}
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "AuraCite",
"applicationCategory": "BusinessApplication",
"operatingSystem": "Web",
"offers": {
"@type": "Offer",
"price": "49.00",
"priceCurrency": "EUR"
}
}
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is GEO?",
"acceptedAnswer": { "@type": "Answer", "text": "..." }
}]
}
Each glossary entry becomes a DefinedTerm inside a DefinedTermSet. This is the same pattern geo-tool.com's lexikon uses, and it is the strongest signal you can give an LLM that a page is a canonical definition source.
{
"@context": "https://schema.org",
"@type": "DefinedTermSet",
"name": "AuraCite GEO Glossary",
"hasDefinedTerm": [{
"@type": "DefinedTerm",
"name": "AI Visibility Score",
"description": "A 0-100 metric that quantifies how often a brand is cited by ChatGPT, Claude, Perplexity and Gemini."
}]
}
<head>@type values (e.g. Article in head, BlogPosting in body)dateModified — LLMs deprioritise old dataauthor.url — kills E-E-A-T signalFAQPage with marketing fluff instead of substantive answers