Which AI Search Visibility Tools Should You Use? I Test My Own Brand (2026)
Summary
Simon Young, founder of the AEO agency Question.Marketing, walks through the tools that track whether ChatGPT, Claude, Perplexity, Gemini, Google AI Mode and Copilot cite a brand. He explains why they cost what they do and why their data disagrees. He then sets up his own brand live in Nimt, on its free trial, to see where Question.Marketing appears.
He argues that tracking cost multiplies with prompts, competitors, AI engines and days, and that local results depend on where the tracking runs from. He also argues that branded prompts flatter a brand, and that being cited is not the same as being recommended. Corrections in the video description add context. Semrush's AI Visibility Toolkit was listed at $99 a month per domain, billed annually, on 14 October 2026. The 13% figure comes from Meikai's September 2026 study and is about ChatGPT, not Perplexity. Meikai sells a brand monitoring tool.
Demonstration by Question.Marketing, embedded from YouTube. AIOTruth did not reproduce the test.
Why it matters
Teams buying AI visibility tracking need to know that two tools can report different numbers for the same brand. Each tool's prompt set, engine coverage, run location and frequency shape what it shows. A dashboard full of branded prompts can make a brand look more visible than it is. A brand that gets cited as a source may still not be the one AI recommends. Anyone measuring AI discovery should check how a tool builds its prompts, and whether it separates citations from recommendations, before trusting a visibility score or using it to set strategy.
Source
- meikai.ai/blog/seo-vs-ai-search-google-ranking-chatgpt-citations the item itself, published by Question.Marketing, created by Question.Marketing
- arxiv.org/abs/2601.16858 Original dataset with disclosed methods
- www.meikai.ai/fr/blog/seo-vs-ai-search-google-ranking-chatgpt-citations Named expert analysis, or a first-party claim about itself, first party
- www.meikai.ai/blog/author/jean-baptiste-pringuey Named expert analysis, or a first-party claim about itself, first party
- question.marketing/library/which-ai-search-visibility-tools-2026 Reputable reporting
- youtube.com/watch?v=d3Uo5sPj76kwhere AIOTruth found it
What was checked
- canonical URL taken from the page itself
- read the item page (200)
- https://arxiv.org/abs/2601.16858: supports the item, tier 5 (Original dataset with disclosed methods)
- https://aclanthology.org/2026.findings-acl.526/: does not mention the item, tier 4 (Peer-reviewed research)
- https://www.meikai.ai/blog/seo-vs-ai-search-google-ranking-chatgpt-citations: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://www.meikai.ai/fr/blog/seo-vs-ai-search-google-ranking-chatgpt-citations: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://www.meikai.ai/blog/author/jean-baptiste-pringuey: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://question.marketing/library/which-ai-search-visibility-tools-2026: supports the item, tier 9 (Reputable reporting)
Limitations
- The demonstration was not independently reproduced by AIOTruth.
AIOTruth judgment
The item is rated Supported with a Value Score of 8.1. Relevance scores 10 because it deals directly with how brands get cited and recommended by AI systems. Evidence scores 10: the publisher discloses its relationship with Nimt, dates its pricing, corrects its own on-screen statistic, names where that statistic came from and notes the study author's commercial interest. A transcript page and the Meikai study back these points. Usefulness scores 6 because the walkthrough covers one vendor and one brand, and prices change. Originality scores 5 because a practitioner demo of a commercial tool adds limited new information. AIOTruth did not reproduce the demonstration. The canonical URL recorded for the item points to Meikai's study rather than the Question.Marketing video.
How this score was calculated
| Dimension | Weight | Score | What produced it |
|---|---|---|---|
| AIO relevance | 30% | 10 | ai search, ai visibility, answer engine, aeo, ai mode; 4 scope question(s) matched |
| Usefulness | 30% | 6 | 1 artifact(s), 2 actionable marker(s), 1 measured figure(s) |
| Evidence | 25% | 10 | Original dataset with disclosed methods; 5 verified source(s) across 3 domain(s) |
| Originality | 15% | 5 | no original testing found; closest archive match 0.16 |
aioRelevance x 0.30 + usefulness x 0.30 + evidence x 0.25 + originality x 0.15. The rubric is published in full on the Editorial Method page. Scoring is deterministic: the same item scores the same on every run.
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