VisibAI
Are you in AI answers? Find out and fix it in minutes
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VisibAI - Are you in AI answers? Find out and fix it in minutes

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VisibAI shows whether your business appears when people ask AI for recommendations, and helps you fix it. It runs queries across six AI platforms (ChatGPT, Perplexity, Claude, Gemini, Mistral, You.com), scores your visibility 0-100, reveals which competitors show up instead, and returns a prioritized fix list plus ready-to-ship fix files and a branded report. One-off audits or monthly tracking. White-label for agencies. EU-hosted and GDPR-native.

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The visibility gap in AI-generated answers is real and most brands have no idea they're invisible. Excited to see tooling for this, does VisibAI track citation sources across different LLMs or just ChatGPT/Perplexity?

Maker
Hi 👋 Thanks, and yes, “they have no idea” is the whole reason this exists. Coverage isn’t just ChatGPT/Perplexity, we run across the major answer engines including Claude and Google’s AI too, so you see your visibility per platform, not one blended guess. Today we surface which competitors get named instead of you in those answers. Source-level citation tracking (the exact pages an engine pulls from, per LLM) is the next big piece I’m building, since Reddit and a handful of sources punch way above their weight in what AI cites. If that’s the angle you care most about, tell me which engines matter to you and I’ll factor it into how I prioritize it.

The competitor-citation angle is what makes this more than a vanity score for me — seeing who AI names instead of you points straight at the content gap. Since the six engines pull from different sources with different recency windows, do you surface which specific source got cited (a Reddit thread, a listicle, a competitor's page) so the fix list can target that, or is it focused on the on-site schema/llms.txt side?

Maker
That’s the exact line I care about too, a vanity score is useless, “here’s who AI names instead of you” is a to-do list. Straight answer on where we are today: we surface the competitor that got named, per query, plus the on-site fixes (schema, llms.txt, crawler access, FAQ). What we don’t yet expose is the specific source the engine cited, the actual Reddit thread vs listicle vs competitor page. That’s deliberately the next build, because you’re right that it’s the difference between “write about this” and “go get mentioned on this exact page.” It matters even more given your recency-window point: a Reddit thread cited by Perplexity this week is a totally different fix than a 2-year-old listicle ChatGPT leans on. Surfacing the source per engine is what makes the fix list targetable instead of generic. It’s high on the roadmap and clearly several of you want it most, so it’s moving up. Appreciate you sharpening the case for it.
It looked promising, but after testing it and filling the entire form it gave me an “Audit failed” error, and I had to start all over again since the data entered in the form was not saved…
Maker
Hi 👋 sorry, that’s a genuinely bad first run and the lost form data makes it worse. Thank you for flagging it instead of just bouncing. Can you drop the URL you were auditing (here or DM - )? I’ll trigger the run on my side, confirm it completes, and send you the result directly so you’re not blocked.

GEO is going to be as important as SEO was 10 years ago and most businesses haven't even started thinking about it. the multi-platform scoring across chatgpt, claude, perplexity etc is smart because your visibility can vary wildly between them. one model might recommend you and another might not even know you exist. curious how fast the fix recommendations actually move the needle. with traditional SEO you're waiting weeks for changes to reflect. how quickly do AI models pick up on changes you make to your site or content?

Maker

 Love the SEO parallel, that's exactly the bet.

Two speeds on the fixes: technical changes (schema, llms.txt, crawler access) show up in days to weeks on the grounded engines that retrieve live, like Perplexity and Google AI.
The base ChatGPT/Claude models only shift when they retrain, so that side is slow.
The bigger lever, getting cited in the sources they pull from (Reddit, G2, listicles), is a slower build but it's what sticks. We re-scan over time so you see which change moved which engine, instead of guessing.

Sounds interesting but my free test audit failed.

Maker
Fixed ✅ Audits are running clean again. That was a provider quota ceiling under launch traffic, not a product bug, and it’s cleared now. If your test failed earlier, please give it another go, it’ll work this time. Thanks for bearing with me, real-time launch debugging is half the fun 😅

 thanks for the quick fix. I can feel you with the debugging stuff pretty well. :D
Next issue is "Rate limit exceeded. Please try again later." Sure will be working soon again.

Maker
😂 you’re following the whole debugging saga live, respect 😄 Good catch, that one’s a different beast: not the provider this time, it’s my own rate-limiter being too conservative under launch traffic. Give it another go now and it should complete (free audits are landing fine on my side this second). If you need anything else or further information, just comment and always available via email as well -

This looks like a really fantastic product. I just filled out the detailed form to run an audi for my site but it immediately failed. See attached screenshot. I checked the console logs to see if there was any technical feedback but I don't see any. I still want to try your product because it sounds fantastic. Let me know if there is something I should do differently or if there is a way for me to share technical feedback like checking console logs.

Maker
Just replied to another comment, everything should work now. Happy to assist personally and give you any further assistance here or via DM - Thank you again

 I tried again and now the error I am getting is "Rate limit exceeded. Please try again later." See screenshot. I'm not mad, I'm very interested in trying the product whenever these issues are resolved.

Maker

 Everything should work now. But since you mentioned you'd like to give the solution a try, I'd be glad to run a multi-platform audit for you myself. Just send your URL to and I'll email you the full report, no form, no queue. You shouldn't have to fight your way in three times.

And if you'd rather run it yourself, I'm happy to enable a one month trial for you, just let me know.

It honestly hadn't hit me how much people now ask an assistant before they ever open a search bar. Thanks a lot for this product Francesco!

Nice work shipping this! What made you decide to build this now?

Maker

Thanks  =)

Timing, mostly. Buyers have quietly shifted from googling to asking ChatGPT, Perplexity and Claude for recommendations, and businesses have no idea whether they show up in those answers. SEO tools can't see it.

That blind spot is brand new and growing fast, so it felt like the right moment to build the thing that measures it and tells you what to fix.

The interesting part isn't the score, it's knowing what to do next and whether it actually worked. How do you decide which queries to test so they reflect real buyer behavior? And once a team implements the fixes, what's the feedback loop? AI visibility doesn't have a Search Console equivalent, so I'm curious how you help teams know they're actually improving. Congrats on the launch!

Maker

 two great questions =)

Queries: we generate them from your industry, sub-category and buyer context, then split by funnel stage (awareness, consideration, decision) so they mirror how real buyers actually ask, not just branded terms. You can edit or add your own before the run.

Feedback loop: you're right there's no Search Console for this, so we are it. We re-scan over time and show which queries flipped and on which engine after you apply fixes. The clean before/after attribution is the piece I'm actively tightening right now, since proving it worked is the whole point.

The hard part with a visibility score like this is LLM nondeterminism — ask ChatGPT the same recommendation query twice and you can get different brands back. Do you sample each query multiple times and average into the 0-100, or is it a single-shot snapshot? And are the six platforms hit through official APIs or logged-in scraping, since that changes whether the result matches what a real signed-in user actually sees.

Maker

 thank you for your comment!

Sampling: single-shot snapshot today, not averaged. You're right that non-determinism means one run isn't gospel, so multi-sampling and averaging is high on my list. For now we re-scan over time to smooth the noise.

Access: official APIs, not scraping. Reproducible and clean, but it's the API model's answer, not a pixel-perfect match to a signed-in app session. Treat it as a consistent proxy for the model's knowledge.

Makes sense — official APIs over scraping is the right call for reproducibility, and framing it as a proxy for the model's knowledge rather than a live signed-in session is honest. On the re-scan-over-time approach: do you surface the variance to the user, or just the latest smoothed score? A confidence band would tell me whether a 1-point move is real movement or just run-to-run noise.

Maker

 
Spot on, and you've named the exact gap. Right now we surface the latest score and the trend line, but not a variance or confidence band, so today a 1-point move and real movement look the same, which isn't good enough.

A confidence interval is the right answer, and it's the natural payoff of the multi-sampling work: once each query is sampled several times, we get a distribution per query, which gives both a more stable score and the band around it to tell signal from run-to-run noise.

That's the direction this is heading. Appreciate you pushing on it, this is exactly the kind of rigor the category needs.

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