AI Search Console helps SEO and GEO teams replace manual AI visibility checks with repeatable data. Track brand mentions, rankings, share of voice, competitors, and cited sources across ChatGPT, Claude, Gemini, and Perplexity. Analyze visibility at the individual prompt level, find content and citation gaps, and generate client-ready reports without spreadsheets or screenshots.
We built it after repeatedly hearing the same question from SEO agencies and their clients:
“Are we actually showing up in ChatGPT?”
The usual way to answer was surprisingly manual: run a few prompts, take screenshots, copy the results into a spreadsheet, and try to guess whether visibility was improving.
That approach breaks down quickly. AI answers vary by prompt, model, market, and time. A handful of manual checks cannot reliably show your share of voice, explain why a competitor appears more often, or identify which sources influence the answers.
AI Search Console turns that process into repeatable analytics. It monitors how your brand and competitors appear across ChatGPT, Claude, Gemini, and Perplexity, down to the individual prompt and cited source.
What you can analyze:
– Prompt-level brand mentions and rankings – Multi-model share of voice – Competitor visibility and content gaps – The domains and pages cited in AI answers – The prompts where your brand appears and where it is missing – Trends over time and client-ready reports
This matters because AI visibility is not determined only by your website or Google rankings. AI platforms may rely on review sites, editorial articles, communities, comparison pages, and other third-party sources. You can rank well in traditional search and still be mostly absent from AI-generated recommendations.
AI Search Console helps teams move from:
“Let’s manually check a few prompts”
to:
“Here is our visibility, here is the gap, and here are the sources and prompts behind it.”
It’s built primarily for SEO/GEO agencies and brands that need a measurable way to understand and improve their presence in AI search.
I’ll be here all day and would genuinely love your feedback. Ask me anything 🙏
We've been stuck piecing together how our brand shows up in ChatGPT and Perplexity by manually running queries, which doesn't scale past a handful of terms. Does the tool track how often the same brand gets recommended across different phrasings of a query, or just exact-match prompts? Congrats on shipping!
Citation mapping is probably my favorite part of this. As AI search becomes more important, understanding why your brand gets mentioned feels just as valuable as knowing if it gets mentioned. Congrats on the launch!
Looks good! How different is it vs other AEO solutions? What would be your USPs?
Congratulations on the release. I've been following this space closely myself. The biggest challenge we're currently facing is:
The output gap between consumer-facing (C-end) products and vendor API platforms.
Our GEO system integrates through the vendors' open platform APIs, while end users interact with the vendors' own C-end products. Between the two layers, the "invisible capabilities" built into C-end products—such as system prompts, knowledge bases, and web search—are not exposed through the open platforms. As a result, the model responses we receive are inherently weaker than what the C-end delivers, which undermines the accuracy of our downstream analysis.
In short: responses from the open API systematically underperform the C-end, primarily because the underlying prompts and knowledge base configurations are opaque and inaccessible to us.
This is useful. AI search is making SEO feel messy again, especially when you’re trying to understand why some sources get cited and others don’t.
the same prompt to the same model can return different citations run to run, that's the part i'd worry about. do you sample each prompt multiple times before logging a rank/share-of-voice number, or is it one call per prompt per check?
same prompt can get a different citation set run to run on the same model. how do you separate real ranking signal from just LLM response variance, averaging multiple runs per prompt?
Looks super exciting, congrats on the launch! Citation mapping for AI search is such a smart angle.
Congrats team! Citation mapping is going to matter a lot more this year. Which AI engines are you covering at launch?
Presence and correctness are two different problems and only one of them is in these metrics. Being cited says you were in the answer. It does not say the answer described you correctly.
The version that costs money is a confident wrong attribute. The answer says you integrate with something you do not, or that there is a free tier when there is not. The reader never opens the source, so they arrive already believing it, and share of voice scores that as a good day.
Do you capture what the answers claim about a brand, or only whether it appeared?
This lands at a good moment, it feels like every marketing team out there is suddenly asking how their brand shows up inside ChatGPT answers. The client-ready reports sound especially handy for agencies. Which of the four engines gives you the most stable results week to week, or do the rankings jump around a lot?
We looked at building a scrappy version of this in-house a few weeks ago and stopped, for a reason I'd be curious how you handle. At zero citations there is nothing to measure. Our site is young enough that every prompt came back with no mention, which is a valid data point exactly once.
So the tool seems to earn its keep somewhere above a zero baseline. Do you have a sense of where that floor sits, or do early-stage users get value from the competitor and cited-source side while their own mentions are still empty?
About AI Search Console on Product Hunt
“Prompt analytics and citation mapping for AI search”
AI Search Console launched on Product Hunt on July 30th, 2026 and earned 522 upvotes and 279 comments, earning #3 Product of the Day. AI Search Console helps SEO and GEO teams replace manual AI visibility checks with repeatable data. Track brand mentions, rankings, share of voice, competitors, and cited sources across ChatGPT, Claude, Gemini, and Perplexity. Analyze visibility at the individual prompt level, find content and citation gaps, and generate client-ready reports without spreadsheets or screenshots.
AI Search Console was featured in Marketing (466.7k followers), SEO (38k followers) and Artificial Intelligence (475.2k followers) on Product Hunt. Together, these topics include over 199k products, making this a competitive space to launch in.
Who hunted AI Search Console?
AI Search Console was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AI Search Console stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hey Product Hunt! 👋
I’m Ilia, founder of AI Search Console.
We built it after repeatedly hearing the same question from SEO agencies and their clients:
“Are we actually showing up in ChatGPT?”
The usual way to answer was surprisingly manual: run a few prompts, take screenshots, copy the results into a spreadsheet, and try to guess whether visibility was improving.
That approach breaks down quickly. AI answers vary by prompt, model, market, and time. A handful of manual checks cannot reliably show your share of voice, explain why a competitor appears more often, or identify which sources influence the answers.
AI Search Console turns that process into repeatable analytics. It monitors how your brand and competitors appear across ChatGPT, Claude, Gemini, and Perplexity, down to the individual prompt and cited source.
What you can analyze:
– Prompt-level brand mentions and rankings
– Multi-model share of voice
– Competitor visibility and content gaps
– The domains and pages cited in AI answers
– The prompts where your brand appears and where it is missing
– Trends over time and client-ready reports
This matters because AI visibility is not determined only by your website or Google rankings. AI platforms may rely on review sites, editorial articles, communities, comparison pages, and other third-party sources. You can rank well in traditional search and still be mostly absent from AI-generated recommendations.
AI Search Console helps teams move from:
“Let’s manually check a few prompts”
to:
“Here is our visibility, here is the gap, and here are the sources and prompts behind it.”
It’s built primarily for SEO/GEO agencies and brands that need a measurable way to understand and improve their presence in AI search.
I’ll be here all day and would genuinely love your feedback. Ask me anything 🙏
💜 You can also try our demo: https://app.search-console.ai/demo