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AI Answer Engine Citation Behavior: The GEO-16 Framework Explained

By Neural Command, LLC — Santa Monica, CA

Published · Updated

Interactive GEO-16 Framework Algorithm

flowchart TD A["START"] --> B["Metadata Check"] A --> C["HTML Structure"] A --> D["Structured Data"] B --> E["Provenance"] C --> F["Risk Management"] D --> G["RAG Optimization"] E --> H["Calculate GEO Score"] F --> H G --> H H --> I["Brave: 78% Citations"] H --> J["Google: 72% Citations"] H --> K["Perplexity: 45% Citations"] classDef startNode fill:#00ff00,stroke:#333,stroke-width:2px,color:#000 classDef checkNode fill:#0066cc,stroke:#333,stroke-width:2px,color:#fff classDef calcNode fill:#ffff00,stroke:#333,stroke-width:2px,color:#000 classDef outputNode fill:#ff6600,stroke:#333,stroke-width:2px,color:#fff class A startNode class B,C,D,E,F,G checkNode class H calcNode class I,J,K outputNode

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Click on nodes to explore the algorithm flow. Each node represents a check in the GEO-16 framework.

TL;DR. AI answer engines cite pages they can parse, trust, and verify. Meet GEO-16 thresholds (G≥0.70, ≥12 pillar hits), validate JSON-LD, enforce semantic HTML, expose recency with real dates, and maintain provenance. Pair on-page excellence with earned media.

The New Era of Visibility

Generative engines like Google AI Overviews, Brave Summary, and Perplexity now synthesize answers and attribute only a handful of sources. Citation — not rank — is the new distribution. Our job is to make your page the reliable source models select.

GEO-16, Explained

GEO-16 is a sixteen-pillar scoring model linking on-page quality to citation behavior. It operationalizes six principles: People-First Answers, Structured Data, Provenance, Freshness, Risk Management, and RAG Fit.

Top-Impact Pillars

  • Metadata & Freshness: Visible timestamps and machine-readable dates (datePublished, dateModified, ETag, sitemaps).
  • Semantic HTML: Single <h1>, logical <h2>/<h3>, descriptive anchors, accessible lists/tables.
  • Structured Data: Valid JSON-LD matching visible content (Article/FAQPage/Product/LocalBusiness/Breadcrumb).

What the Data Shows

Engine Mean GEO Citation Rate Avg. Pillar Hits
Brave Summary 0.727 78% 11.6
Google AI Overviews 0.687 72% 11.0
Perplexity 0.300 45% 4.8

Thresholds: G ≥ 0.70 and ≥ 12 pillar hits are associated with a strong jump in cross-engine citations. Odds of citation rise ~4.2x with higher GEO scores.

How We Implement This at Neural Command

  • Automated schema validation and injection per template (Article, FAQPage, Breadcrumb, WebSite).
  • Semantic hierarchy linting and internal link diagnostics.
  • Freshness enforcement — visible timestamps, JSON-LD dates, sitemap lastmod.
  • Provenance checks — authoritative references, link-rot sweeps, canonical fencing.

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FAQ

Is JSON-LD a direct pipeline to AI citations?
It's the machine interface answer engines rely on to interpret your page. It must be valid, complete, and aligned with visible content. It doesn't guarantee citations by itself; earned authority and on-page quality still matter.
Does recency really matter?
Yes. Visible dates + machine-readable dateModified and sitemaps contribute to freshness signals that correlate with higher citation probability.
What about social content?
Social platforms are rarely cited in AI answers. Earned media on authoritative domains and well-structured owned pages outperform social posts for citation likelihood.