+-----------------------------------------------------------------------------------+
|                        AI CITATION EFFICACY RANKING BY SCHEMA TYPE                |
|                                                                                   |
|  [ RANK 1: FAQPAGE & QAPAGE (98% Extraction Efficacy) ] ───────────────────────── |
|  * Provides pre-parsed question-and-answer pairs directly to LLM prompt context.  |
|  * Highest citation conversion for troubleshooting and definitional queries.      |
|                                │                                                  |
|                                ▼                                                  |
|  [ RANK 2: TECHARTICLE & SCHOLARLYARTICLE (94% Extraction Efficacy) ] ─────────── |
|  * Embeds author entity, peer-reviewed citations, and ISO 8601 temporal anchors.  |
|                                │                                                  |
|                                ▼                                                  |
|  [ RANK 3: SOFTWAREAPPLICATION & PRODUCT (88% Extraction Efficacy) ] ──────────── |
|  * Injects exact feature lists, system requirements, and pricing tiers for B2B.   |
|                                │                                                  |
|                                ▼                                                  |
|  [ RANK 4: HOWTO & PROCEDURAL STEPS (82% Extraction Efficacy) ] ───────────────── |
|  * Powers numbered implementation steps in Siri, ChatGPT, and Gemini overviews.   |
|                                │                                                  |
|                                ▼                                                  |
|  [ RANK 5: ORGANIZATION & PERSON GRAPH (78% Trust Weighting) ] ────────────────── |
|  * Provides Wikidata sameAs knowledge graph grounding for machine E-E-A-T.       |
+-----------------------------------------------------------------------------------+

+-----------------------------------------------------------------------------------+
|                        4 PRODUCTION AI SCHEMA BLUEPRINTS                          |
|                                                                                   |
|  1. TECHARTICLE SCHEMA ─────────> Author graph, ISO dates & dependencies.         |
|  2. SOFTWAREAPPLICATION SCHEMA ─> Features, requirements & pricing tiers.         |
|  3. NESTED FAQPAGE SCHEMA ──────> Direct 40-word Q&A pairs for zero-shot RAG.     |
|  4. ORGANIZATION ENTITY GRAPH ──> Wikidata sameAs & publisher trust links.        |
+-----------------------------------------------------------------------------------+

{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "Structured Data for Generative AI Engines: JSON-LD Guide",
  "description": "Technical guide to implementing Schema.org structured data for ChatGPT, Perplexity, and Claude AI search engines.",
  "datePublished": "2026-08-31T00:00:00Z",
  "dateModified": "2026-08-31T00:00:00Z",
  "dependencies": "TypeScript 5.0+, Next.js 15",
  "proficiencyLevel": "Expert",
  "author": {
    "@type": "Person",
    "name": "Sarah Jenkins",
    "jobTitle": "Lead Search Architect",
    "worksFor": {
      "@type": "Organization",
      "name": "BugViso"
    },
    "sameAs": [
      "https://www.wikidata.org/wiki/Q12345678",
      "https://github.com/sarahjenkins"
    ]
  },
  "publisher": {
    "@type": "Organization",
    "name": "BugViso",
    "url": "https://bugviso.com",
    "logo": {
      "@type": "ImageObject",
      "url": "https://bugviso.com/logo.png"
    }
  }
}

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "BugViso",
  "operatingSystem": "All (Cloud Platform)",
  "applicationCategory": "DeveloperApplication",
  "offers": {
    "@type": "Offer",
    "price": "0.00",
    "priceCurrency": "USD",
    "description": "1 free branded PDF audit download per month; extra reports $4.99 on demand."
  },
  "featureList": [
    "Headless Chromium website performance audits under 3G network emulation",
    "Automated WCAG 2.1 AA web accessibility testing with axe-core",
    "Generative Engine Optimization (GEO) 0-100 machine citability scoring",
    "Automated /llms.txt standard manifest validation"
  ],
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.9",
    "reviewCount": "142"
  }
}

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Why is JSON-LD preferred over Microdata for AI search engines?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "JSON-LD consolidates structured data into a single self-contained script tag in the HTML head or body. AI crawlers can parse JSON-LD in single-digit milliseconds without traversing the DOM tree, eliminating parsing latency and layout ambiguity."
      }
    },
    {
      "@type": "Question",
      "name": "Which schema fields improve citation accuracy most in Perplexity?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The acceptedAnswer text in FAQPage, the author sameAs graph in TechArticle, and featureList arrays in SoftwareApplication generate the highest citation accuracy and footnote inclusion rates."
      }
    }
  ]
}

// components/AIOptimizedSchema.tsx
import React from 'react';

interface SchemaProps {
  headline: string;
  description: string;
  datePublished: string;
  dateModified: string;
  authorName: string;
  authorWikidata?: string;
  faqItems?: Array<{ question: string; answer: string }>;
}

export default function AIOptimizedSchema({
  headline,
  description,
  datePublished,
  dateModified,
  authorName,
  authorWikidata,
  faqItems,
}: SchemaProps) {
  const articleSchema = {
    '@context': 'https://schema.org',
    '@type': 'TechArticle',
    headline,
    description,
    datePublished,
    dateModified,
    author: {
      '@type': 'Person',
      name: authorName,
      ...(authorWikidata ? { sameAs: [authorWikidata] } : {}),
    },
    publisher: {
      '@type': 'Organization',
      name: 'BugViso',
      url: 'https://bugviso.com',
      logo: 'https://bugviso.com/logo.png',
    },
  };

  const faqSchema = faqItems && faqItems.length > 0 ? {
    '@context': 'https://schema.org',
    '@type': 'FAQPage',
    mainEntity: faqItems.map((item) => ({
      '@type': 'Question',
      name: item.question,
      acceptedAnswer: {
        '@type': 'Answer',
        text: item.answer,
      },
    })),
  } : null;

  return (
    <>
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{ __html: JSON.stringify(articleSchema) }}
      />
      {faqSchema && (
        <script
          type="application/ld+json"
          dangerouslySetInnerHTML={{ __html: JSON.stringify(faqSchema) }}
        />
      )}
    </>
  );
}

+-----------------------------------------------------------------------------------+
|                        BUGVISO STRUCTURED DATA AUDIT PIPELINE                     |
|                                                                                   |
|  [ Web Application Submitted ] ──> [ FastAPI + ARQ Redis Worker Cluster ]         |
|                                         │                                         |
|                                         ▼                                         |
|  [ 4-STAGE SCHEMA & CITABILITY ENGINE ] ────────────────────────────────────────  |
|  ├── 1. JSON-LD Graph Validator: Asserts syntax for TechArticle, FAQ, SoftwareApp |
|  ├── 2. Schema-DOM Parity QA: Cross-references JSON-LD against rendered HTML text |
|  ├── 3. Machine E-E-A-T Linter: Validates Person sameAs & Wikidata knowledge graph|
|  └── 4. GEO Citability Engine: Evaluates /llms.txt and AI crawler permissions      |
|                                         │                                         |
|                                         ▼                                         |
|  [ COMPOSITE 0-100 GEO SCORE + ACTIONABLE DEVELOPER REMEDIATION PLAYBOOK ]        |
+-----------------------------------------------------------------------------------+