10,000+
queries referenced in Princeton GEO research
GEO Check
Tailored for Content Platforms and Growth Teams
Get found and cited by AI
Breaking past standard technical checkpoints. Our analysis engine unites structured data testing, E-E-A-T evaluations, and leading GEO (Generative Engine Optimization) algorithms to uncover your true visibility in the era of AI search.
+30–115%
AI visibility lift from source citations
Data Driven
Providing actionable and clear optimization guidelines
Transparent Audit Process
The engine is performing multi-layer analysis on your page. You can track real-time progress below.
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How our diagnostic engine evaluates your content
The analysis follows a two-layer model: rapid script checks handle deterministic code structure, while a powerful semantic engine evaluates the meaning and trustworthiness of your text.
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Automated EngineAudit Evaluation Dimensions
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AI Crawlers & llms.txt
Verifies OAI-SearchBot, PerplexityBot, and /llms.txt discoverability protocols.
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Schema & Entity Graphs
Audits JSON-LD microdata, WebApplication markup, and authoritative entity graphs.
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Answer-First & Quotability
Measures TL;DR direct answer density and self-contained snippet extraction suitability.
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E-E-A-T & Trust Pages
Detects trust signals, about/contact/privacy pages, and verifiable external citations.
💡 Tip: Enter any public URL above and click Start Diagnostic to inspect full results
The engine runs deterministic checks & LLM semantic reasoning, outputting an exportable PDF.
GEO Research & Insights
Generative Engine Optimization Insights
Empirical breakdowns, protocol analysis, and answer-first architectures to boost your brand's citations in AI search.
Academic Benchmark+115% Citation Lift
Princeton KDD 2024: How Authoritative Citations Drive +115% Visibility in AI Engines
✓Answer-First TL;DR
Empirical tests across 10,000+ queries show that primary source citations increase AI summary selection by 30% to 115%, while statistical data points provide a +40% uplift.
- Direct Answer Paragraphs (TL;DR) in the first 80 words are 2.4x more likely to be selected by SearchGPT & Perplexity.
- Pairing claims with authoritative citations creates verifiable knowledge triples in LLM graph retrievals.
GEO Research Team•5 min read
Crawler Protocol+40% Stats Boost
PerplexityBot & OAI-SearchBot: Demystifying Modern AI Retrieval Crawlers
✓Answer-First TL;DR
AI search bots bypass client-side render cycles to save latency. Serving clean semantic HTML5 with microdata reduces crawler timeouts and guarantees immediate ingestion.
- Ensure robots.txt explicitly permits OAI-SearchBot, PerplexityBot, and ClaudeBot while blocking bulk scraping bots.
- Time-to-first-byte (TTFB) under 400ms is critical for real-time RAG ingestion loops.
Infrastructure Lab•4 min read
Schema Strategy3x Entity Match
Next-Gen Schema.org for LLMs: Beyond Basic Rich Snippets into Knowledge Graphs
✓Answer-First TL;DR
Generative engines look for explicit entity graphs. Utilizing 'about', 'mentions', and '@id' URIs establishes permanent semantic relationships that survive LLM context tokenization.
- Nest 'about' and 'mentions' properties linking to authoritative Wikidata or Wikipedia URIs.
- Implement FAQPage and HowTo schemas with direct single-sentence answer pairs.
Semantic Web Guild•6 min read
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Visit Blog Hub →Explore all GEO and AI Crawler Research Briefs
Includes SearchGPT readiness checklists, Perplexity guidelines, and Schema templates
Layer 1 — Technical SEO Foundation
Crucial elements like robots.txt, sitemap.xml, canonical tags, heading hierarchies, schema markup, and /llms.txt configurations are algorithmically validated.
Layer 2 — Content Semantic Reasoning
The engine evaluates E-E-A-T by assessing author expertise, credible citations, content freshness, and depth of topical specificity.
GEO Citability Architecture
Instead of measuring keyword density, the engine scores your page's potential to be quoted by AI based on answer-first structures, data statistics, strict citations, and snippet readiness.
Core Logic and Academic References
Our assessment model extends foundational SEO guidelines and is deeply rooted in cutting-edge academic AI citation research, aimed at elevating machine readability.
Research-backed GEO signals
Princeton KDD 2024 research tested more than 10,000 queries and found that source citations can raise AI visibility by +30–115%, statistics by about +40%, and quotation additions by roughly +30–40%.
Therefore, our auditing methodology favors answer-first sections, machine-readable schema, authoritative external links, and self-contained paragraphs instead of keyword stuffing.
Citations
FAQ for growth teams
How is GEO different from classic SEO?
Classic SEO typically prioritizes indexability and organic search results pages, whereas GEO focuses on optimizing content to be easily summarized and cited by conversational AI and generative search functions (e.g., Perplexity, AI Overviews).
Why should my site include an llms.txt file?
Having a well-formatted /llms.txt file natively provides language models with the clear bounds and structured context they inherently prefer, maximizing your content's discoverability.
How is the E-E-A-T score evaluated?
Signals like author expertise, transparent trust pages, verifiable references, and temporal freshness are mathematically tallied, but the final dimension focuses on whether the content relies on specific evidence versus generic summaries.