AEOLens Research distills what answer engines actually use to discover, trust, cite, and recommend content. The focus is operational: structural signals, repeatable patterns, and the changes teams can implement now.
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A practical research briefing on the structural signals answer engines use when they choose which pages to cite.
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Original studies, signal analysis, and model-specific citation patterns.
AI search engines operate on fundamentally different mechanics than traditional search. This guide explains the full pipeline from AI crawler to generated citation — covering indexing, retrieval, trust evaluation, and citation selection across ChatGPT, Gemini, Claude, Perplexity, and Grok.
ChatGPT does not rank pages — it selects passages from sources it can access and trust. This research breakdown covers the specific signals that influence ChatGPT citation selection, from GPTBot access to FAQ schema to direct-answer prose structure.
Gemini (Google) weights trust and freshness signals more heavily than any other major AI engine. This research guide covers E-E-A-T signals, Google-Extended crawler access, freshness requirements, and the structural changes that most improve Gemini citation rates.
Claude (Anthropic) consistently prioritises factual density, explicit entity framing, and semantic clarity in its citation behaviour. This research guide covers the structural and content signals that drive Claude citation rates, and how Claude differs from ChatGPT and Gemini in source selection.
Pages with FAQ schema appear more frequently in AI citation sets.
Author entity markup correlates with stronger citation trust signals.
Recently updated pages are cited more often for high-change topics.
Monthly insights on AI citations, answer engines, and visibility trends — field notes, model updates, and structural changes that matter most.
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