Agencies, strategists, and operators
AI Citation Benchmark Index — Q2 2026
Aggregate AI-Readiness scores and citation rates across the Aeonic fleet
This index summarizes how sites in the Aeonic fleet score on technical AI-readiness factors and how often brands are mentioned when major answer engines respond to real buyer-style queries. Figures below mix live fleet aggregates (where noted) with directional ranges we refine each quarter as scan volume grows.
What this benchmark measures
AI-Readiness is a 13-factor homepage score derived from the same citability model used in product scans: content depth, direct answers, statistics, source citations, lists and tables, heading structure, FAQ signals, schema markup, crawl access, trust signals, readability, social meta, internal linking, and HTTPS. Each factor earns partial credit toward a 0–100 overall score.
Citation tracking runs the same query packs against four engines—ChatGPT, Claude, Perplexity, and Gemini—with logged mention rates and positions so teams can tie page-level fixes to observable inclusion in AI answers over time.
Score distribution
The following ranges are placeholder fleet aggregates for Q2 2026; we will replace them with live rolling statistics as coverage stabilizes.
| Metric | Value |
|---|---|
| Median AI-Readiness | 52/100 |
| Top quartile | 74+ |
| Bottom quartile | Below 38 |
Citation rates by engine
Approximate mention rates (brand cited or clearly referenced in the answer) from recent benchmark batches using comparable query mixes:
| Engine | Approx. mention rate |
|---|---|
| ChatGPT | ~12% |
| Perplexity | ~18% |
| Claude | ~8% |
| Gemini | ~15% |
Key findings
- Schema and FAQ signals correlate strongly with first-page inclusion when engines synthesize “best vendor” and comparison prompts.
- Direct answers and citable statistics raise mention odds on Perplexity and Gemini, which favor quotable, number-backed passages.
- AI crawl access and trust markers track with sustained citations across sessions—especially where models re-ground to the open web.
- Heading structure and internal linking associate with more stable mentions as context windows fill and models pick canonical site paths.
Go deeper
For methodology and population-level correlation between score changes and citation-rate changes, see the live proof study. To run the same 13-factor model and four-engine check on your domain, use the free AI-Readiness scan.
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