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Content Creators · Visibility
Citation rank is the new page rank. Meikai measures how often your content is cited, in which topics, at what rank, and how your share of voice compares to competing publishers across ChatGPT, Gemini, Perplexity, and Grok.
50–65%
of cited domains are earned media
85%
in high-intent queries
4
LLMs monitored
Meikai tracks which publisher and social domains get cited in AI-generated answers for the queries that matter to you, and shows exactly where you stand against well-known competitors.
We monitor citation frequency by LLM (ChatGPT, Gemini, Perplexity, Grok), topic vertical, and intent category. You see your share of voice in absolute terms, not just a rank.
Runner's World
Your domain
REI
Tom's Guide
Reddit r/running
RunRepeat
YouTube
Editorial teams need to know which topics drive AI citations, which intent categories matter most, and where authority gaps exist. Meikai turns AI visibility data into clear editorial direction.
We identify the prompt types and topic clusters where structured, in-depth editorial coverage increases LLM pickup so you can prioritise depth and authority in the areas that matter inside AI systems.
| Topic | AI demand | Coverage | Recommended action |
|---|---|---|---|
Best shoes for half marathon Product discovery | 94 | Partial | Deepen with race-day test data |
Carbon plate shoes comparison Comparison | 87 | Gap | →Create structured comparison guide |
Nike ZoomX technology explained Education | 79 | Strong | Maintain & refresh annually |
Running shoes for overpronation Problem-solving | 74 | Absent | →New article — high priority |
Trail running shoe guide 2025 Discovery | 63 | Partial | Add structured entity data |
How to break in running shoes How-to | 56 | Strong | No action needed |
Editorial teams do not need another telemetry feed. They need to know which desks are being cited and which are not. Citations are grouped by topic, not by URL, so an editor can see at a glance whether technology coverage is holding its position while finance slips, across ChatGPT, Gemini, Perplexity and Grok.
That grouping makes coverage gaps visible. When a model composes an answer for a high-intent question it leans on a small number of pages that state things clearly, and reading citations by beat shows where your reporting is doing that work and where a competing publication is doing it instead.
The same view exports as a topic-level summary suitable for a morning conference or an editorial planning meeting, without asking writers to interpret technical SEO output.
The empty beats matter more than the leading ones, because those are commissioning decisions.
Finance and Sport are the coverage gaps. Read by URL, that pattern is invisible.
Share of voice for a publisher is a comparison, not an absolute. The measure that matters is how often models cite you rather than a rival on the same set of topic questions, which is the difference between being a source models rely on and being one they occasionally reach for.
Models refresh their retrieval and weighting on different schedules, so a title can lead in one engine and be absent from another on the same story. Tracking each engine separately, on a stable question set, shows which formats sustain citation rank over time and which spike once and fade.
Rival titles are named, so the comparison is with the newsroom you actually compete against.
This month, by engine
Same stories, same questions, four different answers. Gemini is the outlier an averaged score would have hidden.
Generative engines lean on independent editorial sources when composing answers, because a publisher can compare, test and qualify in ways a brand cannot credibly do about itself. Earned media accounts for 50–65% of cited domains across intent categories, rising to around 85% in high-intent queries, which is the position a publisher is competing for.
A search ranking places your article in a list a reader chooses from. A citation means a model used your reporting to compose the answer itself, and it may do that without the reader ever reaching your page. The two move independently, so a title can rank well for a story and still be absent from the answers about it.
No. Citation patterns vary significantly by model, intent, topic, and content freshness. Meikai tracks these differences systematically across models so you understand your position in each one.
Meikai monitors ChatGPT, Gemini, Perplexity, and Grok continuously, with more models added as the landscape evolves.
Citations are grouped into topic clusters, not listed by URL, so each desk can see its own citation frequency and rank across ChatGPT, Gemini, Perplexity and Grok, and spot the beats where coverage is not being picked up.
By measuring how often each domain is cited on the same set of topic questions. Comparing on an identical question set turns citation counts into a meaningful share of voice against named rivals.
Yes. Reporting is produced as topic-level summaries showing citation rank and trajectory by beat, intended for editorial planning, not technical SEO review.
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