How We Track Our Own AI Visibility With Answer Radar

1What is the project, and what does it ask?

The project tracks linkeddit.com with the brand name Linkeddit, in the United States, in English. It has 21 approved buying questions and three we removed. Roughly half are unbranded category questions of the kind a buyer types before they know we exist: best competitive intelligence tools for B2B SaaS, best AI visibility tools, best AI brand monitoring tools, Klue and Crayon alternatives, how to monitor brand visibility in AI search. The rest name Linkeddit and ask how it compares, what it costs, or which option suits a small team.

Branded questions tell you what an engine says about you. Unbranded questions tell you whether it names you at all, and that second number is the one that moves pipeline. We keep both in the set so the run can show the gap between them.

2What came back on 6 September?

Run de56db16 asked all 21 questions of ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews within seven minutes, starting 21:04 UTC. 105 samples were requested. 99 returned before the run stalled with 6 outstanding (1 Claude, 2 Perplexity, 3 Google AI Overviews), and the run still read running when this was written. The numbers are over the 99.

Source: Answer Radar, project 441e8df6, run de56db16, metrics as returned by answer_metrics_get on 6 September 2026. Presence and recommendation are over grounded answers only.

Source: the same run. The unbranded count is ours, from the per-question detail in the dataset file.

3Where were we present and recommended?

On the branded questions, mostly. "How does Linkeddit compare to other Reddit lead gens?" got a recommendation from Gemini and ChatGPT, both citing linkeddit.com and leado.co; Claude recommended two other tools while citing our page. "How much does Linkeddit cost" got a recommendation from Gemini, ChatGPT and Perplexity, all citing our own site. "What are the top use cases where Linkeddit outperforms other lead generation tools" got one from Claude, Gemini and ChatGPT.

One unbranded question broke the pattern: "Which competitive intelligence tools use Reddit and review site data?" Claude recommended Crayon, Klue, Linkeddit and Noisely, citing linkeddit.com and noise.ly; Gemini recommended Linkeddit alone, citing g2.com, linkeddit.com and reddit.com; ChatGPT cited linkeddit.com among five sources and recommended nobody. The question names our source layer without naming us, and it is the one place the category questions reached us.

4Where were we absent, and who won?

On every unbranded category question except the Reddit-and-review one, the recommendation went elsewhere, and the sources it went through are the work list.

Three readings. The competitive intelligence questions are decided by a small set of roundups, saashero.net, meertrack.com, unkover.com and industry-lens.com, plus the incumbents' own pages. The AI visibility questions are decided by the vendors' own comparison pages and a handful of aggregators. And "AI citation tracking" is still read as academic reference management by two of four engines, the ambiguity the 4 September baseline flagged and the reason the page targeting that phrase has to disambiguate in its first lines.

A commenter in r/GEO_optimization, replying to a founder three weeks into a public zero-for-six experiment, described the shape we are in on the unbranded rows:

5Which answers can nobody fix with a page?

ChatGPT answered 8 of its 20 questions without searching. Claude answered 2 that way. Those answers are real, and a buyer reads them, but the brands they recommend come from what the model learned before it was asked. For the AEO tools question, ChatGPT's memory answer recommended Semrush, Ahrefs, BrightEdge, Clearscope, MarketMuse, Sistrix and SurferSEO, a list of SEO tools that predates the category.

Answer Radar grades those answers ungrounded and keeps them out of the rates, reporting them separately with the reason not searched. We do not draft fixes for them. We watch the question across runs, and when ChatGPT starts searching for it, the next run tells us which pages decided the answer. The mechanics are in tracking and winning ChatGPT.

The same discipline applies to Google. Google AI Overviews appeared on none of the 16 searches it parsed for our questions. That is recorded as absence, not as a miss, and it is the subject of what Google AI Overviews cite versus ChatGPT and Perplexity.

6What did we change after reading it?

• 1. Fixed the prompt set. Gemini read "What are the best Crayon alternatives?" as drawing materials and recommended Crayola Color Sticks, oil pastels and water-soluble wax pastels beside Klue and Kompyte. The two vendor-name questions get the category word before the next run. A frozen set can still be wrong, and the run is how you find out.
• 2. Filed the run beside the baseline. The 4 September baseline covered 15 questions and four engines; this run covered 21 and five. Both are public dataset files with their run ids, and the next one goes beside them rather than over them.
• 3. Refreshed the published numbers. The behaviour figures on every tracker page, how often each engine searched and how many sources it cited, were re-aggregated over all completed answers including this run, and the Google AI Overviews page moved from pending to its first measured appearance counts.
• 4. Pointed the content work at the cited sources. The roundups and comparison pages in the table above are where the unbranded recommendations were read from. That list, not a visibility score, is the outreach and content plan for the quarter.

What we did not do is attribute anything in this run to the pages we published on 4 September. Two days is not a measurement window, and the re-measurement after a fix reports an observed change, labelled as observed. A commenter in r/SEO, discussing a vendor report that printed a competitor at 100% from fifteen prompts, put the standard we hold ourselves to: day-one zero reports are real, they show you the landscape as it is on that day and nothing more.

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