This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Findabl
Know when AI recommends your brand, using real citation data
Findabl sends real prompts to ChatGPT, Gemini, Perplexity, and Claude, then captures the responses, brand mentions, competitor citations, and publisher domains behind each recommendation. See whether AI cites your brand, who it cites instead, and how citation frequency changes over time. No modeled scores. Direct, auditable measurement. Launch code PRODUCTHUNT gets one free full scan at findabl.app/producthunt.
Findabl started with a question we couldn't answer for our own clients: when a buyer asks ChatGPT for a recommendation in your category, do you come up?
Every tool we tried answered something else. Most put the client's name in the prompt. Ask "is Acme any good" and of course it says yes. That's not visibility, that's politeness. Others sold us AI keyword volume, which can't exist. No AI company publishes query data.
So we ask the question a buyer would ask, with your name nowhere in it, across ChatGPT, Gemini, Perplexity and Claude. You get every response back verbatim, who got named instead of you, and which publisher domains the engine built its answer from.
Three things we don't do.
We track four engines. Some tools claim ten or more. We'd rather do four properly.
We can't tell you what everyone types into ChatGPT. Nobody sees all of it. A consented panel gets you closer, and the good ones say so, but a panel is still the people who joined it.
And we put a margin of error on every number. Ten questions across four engines is forty data points. That's small. We won't call a change real until it clears the margin, which sometimes means telling you we don't know yet.
One thing I'd genuinely like to know from this crowd: if you've tried to measure this, what did you end up trusting, and what made you stop?
Free scan, about a minute, no signup. I'm here all day.
About Findabl on Product Hunt
“Know when AI recommends your brand, using real citation data”
Findabl was submitted on Product Hunt and earned 16 upvotes and 4 comments, placing #27 on the daily leaderboard. Findabl sends real prompts to ChatGPT, Gemini, Perplexity, and Claude, then captures the responses, brand mentions, competitor citations, and publisher domains behind each recommendation. See whether AI cites your brand, who it cites instead, and how citation frequency changes over time. No modeled scores. Direct, auditable measurement. Launch code PRODUCTHUNT gets one free full scan at findabl.app/producthunt.
On the analytics side, Findabl competes within Marketing, SEO and Artificial Intelligence — topics that collectively have 981.2k followers on Product Hunt. The dashboard above tracks how Findabl performed against the three products that launched closest to it on the same day.
Who hunted Findabl?
Findabl was hunted by Thorsten Linz. 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.
Thorsten Linz
Hi Product Hunt, Thorsten here.
Findabl started with a question we couldn't answer for our own clients: when a buyer asks ChatGPT for a recommendation in your category, do you come up?
Every tool we tried answered something else. Most put the client's name in the prompt. Ask "is Acme any good" and of course it says yes. That's not visibility, that's politeness. Others sold us AI keyword volume, which can't exist. No AI company publishes query data.
So we ask the question a buyer would ask, with your name nowhere in it, across ChatGPT, Gemini, Perplexity and Claude. You get every response back verbatim, who got named instead of you, and which publisher domains the engine built its answer from.
Three things we don't do.
We track four engines. Some tools claim ten or more. We'd rather do four properly.
We can't tell you what everyone types into ChatGPT. Nobody sees all of it. A consented panel gets you closer, and the good ones say so, but a panel is still the people who joined it.
And we put a margin of error on every number. Ten questions across four engines is forty data points. That's small. We won't call a change real until it clears the margin, which sometimes means telling you we don't know yet.
One thing I'd genuinely like to know from this crowd: if you've tried to measure this, what did you end up trusting, and what made you stop?
Free scan, about a minute, no signup. I'm here all day.