Aeonic Research
Long-form, citation-rich analysis written for agencies, AEO/GEO practitioners, and SMB decision-makers. Each piece pairs primary research with operational recommendations you can ship the same week.
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Pillar research · By Ali Jakvani, Cofounder
A proprietary methodology for AEO. CSI models why language models cite some sources and ignore others — combining Semantic Retrieval Confidence, Entity Resolution Depth, Contextual Authority Density, Answer Extraction Probability, Multi-Engine Citation Stability, and a fragmentation penalty into one composite score. Pillar research by cofounder Ali Jakvani.
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Agencies, strategists, SMB decision-makers
The unit of competition has changed. In AI search, the contest is for inclusion, citation, summarization, and recommendation. Brands that measure only blue-link performance will miss it.
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AEO/GEO practitioners, content teams
Citation behavior is neither random nor identical across platforms. The pages most likely to be cited combine fresh metadata, semantic HTML, and structured data — but engine logic differs.
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SEO teams, agencies, implementers
Most teams approach AI visibility backward. The durable approach is architectural: build pages so they are easy to parse, easy to trust, and easy to quote from the start.
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Agency leaders, SMB operators
AI visibility often influences the market before the click. ROI is real, but it has to be modeled with more discipline than last-click thinking allows.
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Agencies, strategists, data-driven teams
Fleet-level view of AI-Readiness score bands and citation mention rates across ChatGPT, Perplexity, Claude, and Gemini—plus what factor patterns track with citations.
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Marketing leaders, brand strategists
AI search engines synthesize answers, not links. The competition is no longer for a ranking position — it is for inclusion in the answer itself. This article explains the shift and what to do about it.
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Content teams, SEO managers, agency strategists
Freshness is one of the most actionable and most neglected levers in AI search visibility. This article covers the evidence, the two layers of freshness that matter, and a practical maintenance cadence.
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SEO teams evaluating AI search tools
Semrush is an industry-standard SEO platform. Aeonic measures AI citation readiness. This comparison explains what each does, where they differ, and why serious teams use both.
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Marketing leaders, agency strategists, SMB operators
A consolidated reference of the data that matters for AEO, GEO, and AI citation work — adoption rates, citation patterns, factor weights, engine-by-engine differences, and crawler access. Drawn from peer-reviewed studies, industry reports, and the Aeonic measurement fleet through April 2026.
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Agencies, in-house marketing teams, SMB operators
A direct-answer field guide to the questions agencies, in-house teams, and SMB operators send Aeonic every week. Each section answers one question — AEO vs GEO, llms.txt, schema, freshness, engine differences, and how to measure citation rates.
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Agency owners, SEO leads, enterprise marketing teams
Traditional SEO is not literally dead, but the surface area it optimizes for is shrinking. The unit of distribution has shifted from ranked page to cited passage. Why classical SEO is breaking and what AEO actually requires.
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Agency owners, heads of strategy, technical SEO leads
A practical operator guide for rebuilding agency service lines around AI search. Audit, content engineering, entity infrastructure, and citation monitoring as the four layers that justify a 2026 retainer, plus a 90-day upgrade plan.
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SEO leads, marketing strategists, AEO/GEO practitioners
A technical, operator-level comparison. SEO targets ranked positions. AEO targets citations from AI answer engines. GEO targets inclusion inside generated answers. Where they overlap, where they diverge, and how to sequence implementation.
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Technical SEO leads, AEO practitioners, content engineers
A technical breakdown of the retrieval, reranking, and citation policies that determine which brands AI engines cite. Covers dense retrieval, BM25, cross-encoder rerankers, source diversity, freshness, and entity coherence.
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Platform engineers, technical SEO leads, CTO-level operators
An engineering reference for AI-readable websites. Rendering strategy, content modeling, semantic HTML, structured data, entity graphs, and observability as the six pillars. Skip any one and the system has a hole.
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AEO practitioners, content engineers, agency strategists
Relevance, Evidence, Structure, Identity, Trust. Five measurable signals that determine whether AI answer engines retrieve, synthesize, and cite your content — or discard it. RESIT is a 100-point scoring framework for AEO readiness.
Also from Aeonic
Live data · Updated hourly
Real aggregate citation rates across ChatGPT, Claude, Perplexity and Gemini, plus the full publishing playbook.
See live dataCorrelation study
Population-level Pearson correlation between AI-Readiness score changes and real citation-rate changes. Methodology open.
Read the study