Foglift
Which AI Search Tools Do ChatGPT, Perplexity, Gemini & Google AI Recommend?
June 2026 benchmark. We asked five AI engines 13 buyer questions about AI search visibility tools and measured who got named and cited. The dataset covers share of voice, engine-by-engine disagreement, and the source domains AI engines pull from.
Last updated Data window 2026-05-26 to 2026-06-10 (13 unbranded buyer-intent prompts x 5 AI engines; 59 usable answers)Download CSV
Methodology
We monitored 13 unbranded, high buyer-intent questions about AI search visibility tools, each run against ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews between May 26 and June 10, 2026. That produced 65 question-by-engine slots, 59 with a usable answer, for 91% coverage. For each answer we recorded the tools named and the domains cited as sources. Share of voice is the percentage of the 59 successful slots in which a tool appeared by name or citation. We excluded three competitor-named alternatives prompts from the share-of-voice table to avoid bias, and we excluded Google's vertexaisearch.cloud.google.com redirect wrapper from the source-domain table because it is a proxy URL rather than a publisher.
Why this benchmark exists
Buyers researching AI search visibility tools increasingly skip Google's blue links and ask an AI engine directly: “What is the best AI search monitoring tool?” or “What are the best AEO tools in 2026?” The answer the engine gives is now part of the top of the funnel.
So we ran the experiment. We took 13 high buyer-intent questions in this category and put each one to five AI engines: ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Then we recorded every tool each engine named or cited, and where it sourced the recommendation.
Finding 1: a handful of tools own the category
Across the 13 questions and five engines, a small group of incumbents shows up far more than anyone else. Two purpose-built tools, Otterly and Profound, appear in more than half of usable answers. Semrush is close behind, which shows how quickly general SEO suites are moving into the AI visibility category.
| Tool | Answers named or cited | Share of voice |
|---|---|---|
| Otterly | 34 / 59 | 58% |
| Profound | 32 / 59 | 54% |
| Semrush | 29 / 59 | 49% |
| Peec AI | 21 / 59 | 36% |
| Ahrefs | 21 / 59 | 36% |
| Nightwatch | 9 / 59 | 15% |
| Scrunch | 6 / 59 | 10% |
| HubSpot | 4 / 59 | 7% |
| Writesonic | 3 / 59 | 5% |
| Foglift | 2 / 59 | 3% |
| Brandwatch | 2 / 59 | 3% |
| Goodie, xFunnel, Rankscale, and other long-tail tools | 1 / 59 each | 2% each |
Below the top five, share of voice falls off quickly. If you are a buyer asking an AI engine for a recommendation, you tend to hear the same four or five names no matter how you phrase the question.
Finding 2: there is no single AI search ranking
The five engines disagree sharply, and they do not even agree on how many tools to name. Google AI Overviews and Gemini behave like listicles. ChatGPT behaves like a single recommendation engine.
| Engine | Avg. tools named per answer | Usable answers |
|---|---|---|
| Google AI Overview | 4.8 | 11 |
| Gemini | 4.4 | 9 |
| Perplexity | 2.5 | 13 |
| Claude | 2.1 | 13 |
| ChatGPT | 1.0 | 13 |
The practical implication is simple: optimizing for “AI search” as one surface is a mistake. Each engine is its own channel with its own sources and its own appetite for naming brands. The gap between the most generous engine and the most conservative one is roughly five to one.
Finding 3: recommendations are downstream of a source layer
When engines cited sources for their recommendations, the citations clustered on a recognizable set of domains. After excluding Foglift and Google's redirect wrapper, these were the most-cited source domains:
| Source domain | Times cited |
|---|---|
tryprofound.com | 11 |
zapier.com | 8 |
otterly.ai | 8 |
nightwatch.io | 8 |
aiclicks.io | 7 |
frase.io | 7 |
amplitude.com | 7 |
visible.seranking.com | 6 |
nicklafferty.com | 6 |
seranking.com | 6 |
dageno.ai | 5 |
rankability.com | 5 |
searchable.com | 5 |
builtin.com | 5 |
topify.ai | 5 |
youtube.com | 4 |
semrush.com | 4 |
The pattern is visible even in this small category sample. AI engines read a source layer made up of vendor sites, roundups, reviewer blogs, and aggregators, then name the tools those sources name. If your tool is absent from that source layer, it is much less likely to appear in the answer, even if your own product is strong. The Foglift citation map tracks the source layer behind Foglift's own appearances.
What this means if you want to be recommended
- Treat each engine as a separate channel. A win in Google AI Overviews tells you little about ChatGPT, which names one tool on average. Track all five because the same buyer question returns a different shortlist on each.
- Work the source layer. Getting named in the roundups, reviewer blogs, and aggregators the engines cite is the mechanism by which a tool enters the answer. Your own pages help, but third-party source coverage is part of the recommendation graph.
- Watch the general SEO suites. Semrush and Ahrefs now appear in this category at the same rate as dedicated AI-visibility tools. The category is open to traditional SEO incumbents.
For broader tool selection, compare Foglift against the market in our AI search optimization tools guide, the top AEO and GEO platforms benchmark, and the AI monitoring tools comparison. If you want to monitor the exact signals measured here, review Foglift's engine coverage and monitoring cadence.
Methodology and limitations
This is a snapshot, not a verdict. AI answers vary run to run, brand detection can miss a mention written in an unusual form, and Gemini returned more transient errors than the other engines. The long tail is more likely undercounted than overcounted. We also ran three “alternatives to X” questions that name a competitor. Those are excluded from the share-of-voice numbers above to avoid bias, as are questions that name Foglift directly.
The largest source-attribution limitation is Google's vertexaisearch.cloud.google.com redirect wrapper, which appeared 131 times in the raw citation export. We excluded it from the source-domain table because it is a proxy URL, not a publisher. Resolving those redirects would improve the source-layer analysis for Gemini and Google AI Overview.
Disclosure: This benchmark was produced by Foglift, an AI search visibility tool, using our own monitoring engine. Foglift appears in this dataset in the long tail, at roughly 3% share of voice on these unbranded questions. We publish the leaderboard as the data reports it, including our own position, because a benchmark is useful only if it is honest.
Prompt set
The benchmark used these 13 unbranded buyer-intent prompts. The CSV download includes the aggregate tables behind this report.
- best AEO tools 2026
- best GEO tools for tracking AI search visibility
- best AI visibility tool for agencies managing multiple clients
- AI search monitoring tool with an API
- how to check if ChatGPT recommends my brand
- cheapest AI brand visibility tracking tool
- free tool to check brand visibility in AI search
- best AI search rank tracker for ChatGPT and Perplexity
- best AI search monitoring tool for brands 2026
- tools for tracking citations in ChatGPT/Perplexity/Claude/Gemini/Google AI Overviews
- AI search visibility software comparison
- how to monitor brand visibility in AI search
- best platform to track brand mentions in ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews
Frequently Asked Questions
Which AI search visibility tool is recommended most by AI engines?
In this June 2026 benchmark, Otterly and Profound appeared most often across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews, each in more than half of the answers to unbranded buyer questions, followed closely by Semrush.
Do different AI engines recommend different tools?
Yes, substantially. Google AI Overviews named an average of 4.8 tools per answer while ChatGPT named 1.0. A tool can lead on one engine and be absent on another for the same question.
How do AI engines decide which tools to recommend?
They cite a source layer made up of vendor sites, third-party roundups, independent reviewer blogs, and category aggregators, then name the tools those sources name. Visibility in that source layer strongly shapes visibility in the AI answer.
How was this benchmark measured?
13 unbranded buyer-intent questions were run against five AI engines between May 26 and June 10, 2026, and every named tool and cited domain was recorded. Share of voice is the share of the 59 successful answers in which a tool appeared.
Why does Foglift appear in the long tail of its own benchmark?
Because the benchmark reports the data as observed. Foglift appeared in 2 of 59 unbranded category answers, or roughly 3% share of voice. Publishing that position is part of the point: the report is useful only if the leaderboard is honest.