Will ChatGPT, Perplexity and Gemini cite your page?

AI answer engines like ChatGPT, Perplexity, and Gemini select sources based on structural signals in your HTML — not just content quality. Our Citation Readiness Score evaluates 6 research-backed signals that determine whether your page will be extracted, chunked, and cited by large language models.

The 6 Citation Signals

1. Structured Data (JSON-LD) — Pages with Schema markup are 2.5x more likely to appear in AI-generated answers. We check for FAQPage, HowTo, Article, and Organization types per Google's structured data guidelines.
2. Heading Hierarchy — A single H1 followed by logical H2/H3 sections enables clean chunk extraction for RAG pipelines. Research from Schema.org Article specifications confirms heading structure as a primary parsing signal.
3. Scannable Blocks — Tables, ordered lists, and bullet points provide pre-formatted answer structures that LLMs prefer to cite verbatim.
4. Content Depth — Pages between 1,500-3,500 words score highest. Below 600 words lacks sufficient context; above 3,500 risks diluting the topical focus.
5. External Citations — Pages that link to authoritative external sources demonstrate research depth, a signal correlated with higher E-E-A-T trust in Google's Search Quality Evaluator Guidelines.
6. E-E-A-T Author Signals — Author metadata, Person schema, and byline attribution establish content authoritativeness for LLM trust calibration.

Why Local Analysis Beats LLM Polling

Some tools query ChatGPT or Perplexity directly to check if your site is mentioned. This approach is unreliable — LLM responses are non-deterministic and change with each query. Instead, we analyze the structural signals that cause citation selection, giving you actionable data you can improve against. This methodology aligns with research on W3C web architecture principles for machine-readable content.