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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Maker
📌

Hi everyone 👋

I'm Francesco, founder of VisibAI. I spent years selling SaaS across Europe and watched search behaviour move from Google to AI assistants.

VisibAI tells you whether ChatGPT, Perplexity, Claude and other AI assistants recommend your business when someone asks for one. Then it shows you exactly how to improve.

The problem: for years everyone optimised for Google. Now people open ChatGPT or Perplexity and just ask for a recommendation, and most businesses have no idea whether they show up in those answers.

The solution: a score on its own does not help, so we give you the full benchmark. VisibAI runs automated queries across six AI platforms (ChatGPT, Perplexity, Claude, Gemini, Mistral, ) and shows you:

  • a visibility score from 0 to 100

  • how often you are mentioned or cited

  • which competitors appear instead of you

Then it tells you how to fix it: a prioritised fix list, ready-to-use fix files (robots.txt, schema, FAQ), and an AI action plan.

The benefit: you stop guessing. You see where you stand against competitors in AI answers, and you get concrete steps to climb. Start with a free audit, run a one-off report with no subscription, or go monthly for ongoing tracking and competitor monitoring.

Who it is for:

  • Brands that want to be the name AI recommends in their category

  • Agencies, who can white-label the whole platform under their own subdomain and brand, and run it for every client

You can try it here:

Built in the EU and GDPR-native, which matters to a lot of the teams we work with.

Happy to answer any questions 🙌

 how do you handle non determinism in llm answers like if chatgpt gives a different answer on next refresh, does it average the score?

Maker

 

Good question.

Right now it’s a single-shot snapshot per query, not averaged across multiple runs, so you’re right that a refresh can shift individual answers.

We handle the variance two ways: the score leans on patterns across 30 queries and 6 engines rather than any one answer, so a single flip moves it only slightly, and we re-scan over time so you see the trend rather than treating one run as absolute.

Multi-sampling each query and averaging into the score is high on the build list, it’s the cleanest fix for exactly this, and it’s coming.

 makes sense

It's a great solution.. however some confusions are there, I ran the free audit and got a score of 46, with all zeros on all queries run. So it's not clear where the score came from, my assumption was some comparisons in the query resulted into it, but it wasn't clear in the scoring.. also the compitition mapping was way off, still that's understandable as we are still to create data on it.. now we are a deterministic engine enterprise focused startup so I understand data reaching AI platforms would take time.. still askOdin.app get it's limited organic traffic from founders engagement via social platforms, maybe the traffic is lesser than most consumer startups, still I think the system wasnt able to pinpoint that either.. Still overall I liked the offering, thank you, keep bettering.. good wishes..
Maker
Thank you, this is really useful feedback, and you’ve put your finger on a real weakness in how we present things. On the score: you’re right that it’s not clear, and that’s on us. The 46 is not coming from your AI mentions, those were genuinely zero on the free run. It comes from the technical-readiness half of the score (site structure, schema, crawlability, trust signals). We currently fuse “is your site built to be cited” and “are you actually being cited” into one number, which makes a 46 next to a wall of zeros look broken. We’re splitting those into two separate scores precisely so this stops being confusing. On why the zeros: the free audit only runs ChatGPT, on generic category queries. For a deterministic, enterprise-focused engine like Odin, the big consumer category terms won’t surface you, and a single-engine slice can’t see the founder-led social traffic you mention. A multi-platform run (Perplexity, Claude, Gemini) on queries closer to how your actual buyers search would give a far truer read, the free slice is the narrowest, harshest view. On competitor mapping being off: fair, and noted. For a novel category it leans on weaker signals. Custom competitors on the paid tiers fix most of that.
thanks for explaining..

Hey,
Congrats for the launch.

Quick feedback on my first test so far:

  • 30% of my traffic is coming from GEO/AEO

  • We've done quite extensive work on that and continue

But from what your app tell us: score 49/100

And everything is 0% , not passed, etc.
I don't believe that nothing can be found about us and we get 49/100 score. How is this related?

Like I literally didn't learn anything from it and will not be willing to go further or even paid for that yet.

Hope that's helping you guys improve!

Maker
Hi Really appreciate this, this is exactly the kind of feedback that makes the product better, so thank you for taking the time. You’ve spotted a real UX gap. There are two different things on that screen and we’re not separating them clearly enough: 1. The 0-100 score = how often the AI engines actually name you in answers to buyer-intent queries. Yours at 49 means you’re showing up in a fair chunk of them. 2. The checks showing 0%/not passed = technical optimization items (schema, AI-crawler access, llms.txt, etc.). Those are “headroom,” not “nobody can find you.” You can rank well today and still have those unticked. So the two aren’t contradictory, but the way we present them makes it look like they are. That’s on us to fix, and you’ve just bumped it up the list. That said: if you’re already pulling 30% from GEO/AEO, a 49 sounds low to me, and I’d genuinely like to dig into your specific run. Can you DM me the URL you audited (or drop it here)? I’ll pull the raw query results and tell you exactly which queries you appeared in and which you didn’t. If something’s miscounting, I want to find it. Either way, thanks for stress-testing it. This is more useful than ten “nice launch” comments 🙏

 Done!
Thanks for taking time as well, appreciate that you appreciate raw feedback and get involved into it :)

Maker

 just replied back to your email

 i'd appreciate the feedback as well!

Smart idea. Which AI platform tends to show the widest visibility gaps for most businesses?

Maker

 Thanks for your question =)

Perplexity and Google's AI tend to show the widest gaps.

They lean on fresh, citation-heavy sources, so if your content isn't structured to be cited, you drop out fast. ChatGPT is more forgiving because it leans on broader trained knowledge, so a brand can look fine there and be near invisible on the engines pulling live sources.

That split is exactly why the per-platform view matters more than one blended score.

Really like the framing here. The thing I keep running into with AI visibility is that "not showing up" almost always traces back to plain old ranking and authority. From what I've seen the answer engines mostly pull from pages already sitting in the top 20 for a query, so a page down at position 40 can be perfectly structured and still never get cited.

Does VisibAI separate those two cases? As in "you're invisible because your content isn't quotable" vs "you're invisible because you're not ranking high enough to be in the pool yet." The fix is completely different depending on which one it is, and that's the part I'd personally find most useful.

Maker
today VisibAI tells you that you’re missing from a query and who got cited instead, and it splits results by engine, which gets you partway. The grounded engines (Perplexity, Google’s AI) do live retrieval where your ranking/authority point bites hardest, you’re not even in the candidate pool. The memory-mode engines lean on trained knowledge, where brand presence and being written-about matters more than today’s SERP position. So the per-platform spread is already a soft signal: weak everywhere usually means an authority/pool problem; fine on the memory engines but missing on the retrieval ones points more at quotability and freshness. What it does not do yet is label it for you in plain terms: “you’re not in the pool” vs “you’re in the pool but not quotable.” That’s exactly the diagnosis layer I want to build, and it’s the natural pairing with the source-tracking work (seeing which page/rank the engine actually pulled). Once we know the cited source’s position, we can tell you whether the gap is a ranking job or a content/structure job, instead of handing you a generic fix list.

I like that this starts with a one-time audit instead of asking teams to commit to another monthly SEO tool. My main trust question is reproducibility: does the report show the exact prompts, platform, timestamp, and raw answers behind the score so a team can verify what changed after applying fixes?

Maker
Hi 👋 Exactly why I led with the one-off, thanks for naming it. Honest state: every run is timestamped, stores the raw AI answers behind each query, and shows which queries you appeared in per platform, so the score traces back to real responses, not a black box. The piece I’m finishing: a clean prompt-by-platform grid and a proper before/after diff, so when you re-run after fixes you see exactly which queries flipped and where. The diff logic’s already in the engine, I just need to wire the UI. It’s near the top of the list because verification is the whole point. Run one and I’ll pull your raw per-query results by hand, would value your eye on whether the format hits your team’s bar.

 hey, i went with the one-time angle and agree that it's better that way.

The 'are you in AI answers' question is one I've been thinking about a lot lately - SEO taught us to optimize for search engines, and now there's this whole new discovery layer in ChatGPT, Perplexity, Claude that most tools don't even measure. What sources does this check - just the big three, or does it also cover the AI integrations in search like Bing and Google AI overviews?

Maker

 

Spot on, this layer sits on top of SEO where nobody's measuring.

We cover six engines directly: ChatGPT, Perplexity, Claude, Gemini, Mistral and (Perplexity and retrieve live, closest to that search-plus-AI surface). Google AI Overviews and Bing/Copilot pull differently, so I'm not claiming them until I can measure them properly, both are on the roadmap.

Have you already run your free audit on ? If you'd like a multi-platform audit or a competitor comparison, I'd be happy to set you up with a one month trial.

AEO is still very opaque for many people, so this tool seems like it could be quite helpful.

Maker

 Thanks, that opacity is exactly the gap we're trying to close.

Most people can't even see whether AI mentions them, let alone why. The goal is to make it concrete: here's your score per engine, here's who gets named instead of you, and here's what to fix.

Happy to answer anything if you give it a run =)

checking visibility across six AI engines is smart, the answers diverge way more than people expect. is the fix list stuff you ship to your site, or mostly content nudges?

Maker
Both, and they split cleanly. The shippable stuff is concrete: robots.txt rules to unblock AI crawlers, JSON-LD schema, an llms.txt file, FAQ markup, fixing content that’s hidden behind JS so crawlers can actually read it. We generate those files for you. The content nudges are the slower, higher-impact half: getting cited in the sources these engines actually pull from (G2, Reddit, comparison pages, category listicles). That’s where most of the real visibility gains come from, since the engines lean on third-party mentions more than your own site copy. So the site fixes are the quick wins you ship in an afternoon, and the content/citation work is the compounding play.

the six-platform sweep is the right call - visibility on ChatGPT vs Perplexity vs Claude can look completely different because each pulls from different sources with different recency windows. a score of 60 on one and 20 on another tells you something specific and actionable. the part I'm most curious about: when you show "how to fix it" - is that primarily schema/llms.txt/content changes, or are you also surfacing the competitor citations that are showing up instead of you? knowing who's displacing you in AI answers is probably the most valuable signal for figuring out what content you're actually missing.

Maker
You nailed why the per-platform split matters, a 60 on one engine and 20 on another isn’t noise, it’s a content/recency signal you can act on. On the fix side: both, and you’re right that the competitor angle is the sharper one. We surface the technical layer (schema, llms.txt, AI-crawler access, FAQ), but we also show which competitors are getting named instead of you, per query. That’s the part that tells you what content you’re actually missing, if a rival keeps showing up on “best X for Y” and you don’t, that’s your gap, made concrete. Where I want to push next is going one level deeper: not just who’s displacing you, but which source the engine pulled them from, so the fix moves from “write about this topic” to “you need presence on this specific page/platform.” That’s the build I’m prioritizing. Sounds like you’d have a sharp opinion on it, would welcome it.
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