AI visibility tools tell you whether you showed up in ChatGPT’s answer. They don’t tell you whose version of your category the model believed, which is the thing that actually decides who gets recommended.
Type “AI visibility tools” into Google and you’ll get a wall of dashboards promising to track your brand across ChatGPT, Perplexity, Gemini, and Copilot. Most of them do exactly one thing well: they run a batch of prompts, scan the answers, and tell you whether your name showed up. Some add sentiment. A few trace citations. That’s the category as it exists today.
It’s useful information. It’s also not the thing that decides whether AI recommends you or your competitor. That’s a different question, and almost nobody’s answering it.
The confusion: AI visibility vs. narrative presence
Ask ChatGPT “what’s the best project management tool for a 10-person startup” and it’ll answer confidently, usually naming 3-4 brands with a clear favorite. AI visibility tools will tell you whether you were one of the names. What they won’t tell you is why the model picked a favorite, or what story about your category it’s working from to make that call.
That story, the frame the model has learned about who’s “innovative” versus “legacy,” who’s “for enterprises” versus “for solopreneurs,” is built long before your brand’s name gets typed into a prompt. It comes from years of press coverage, review sites, comparison posts, Reddit threads, and analyst write-ups that the model was trained on and keeps getting served in real time. Visibility tools measure the output. They don’t touch the input.
This is why two brands can run the same tracker and see wildly different realities. You can be mentioned in 60% of answers and still lose every recommendation, because the model mentions you as a caveat (“X is cheaper but less established”) while it recommends your competitor as the default answer. The tracker shows a healthy presence score. The market sees you as second choice.
What existing tools actually measure (and what they miss)
Here’s the honest state of the category, with real pricing and ratings:
| Tool | Pricing | Rating | Best for |
|---|---|---|---|
| Profound | Demo-led, historically $99-5,000+/mo | G2 4.6/5 (~845 reviews) | Enterprise AEO budgets, prompt volume data |
| Peec AI | $95-495/mo | G2 4.9/5 (~12 reviews) | European SMBs, UI-accurate scraping |
| Semrush AI Toolkit | $99/mo add-on + base plan | Mid-4-star (Semrush overall) | Teams already in Semrush |
| Otterly.AI | $29-489/mo | G2 ~4.8 cited | Solo marketers, first GEO program |
| AthenaHQ | $295-499/mo + credits | G2 4.9/5 (~32 reviews) | Funded startups wanting some automation |
| Scrunch AI | $250-1,000+/mo | G2 ~4.6-4.7/5 (~50 reviews) | Mid-market teams with their own execution plan |
| Ahrefs Brand Radar | $328-1,148/mo realistic | No separate listing | Enterprises already on Ahrefs |
| HubSpot AEO Grader | Free | No listing | A one-time diagnostic |
| Brandlight | Sales-gated, ~$199-750+/mo | G2 4.7/5 (19 reviews) | Enterprise white-glove |
| Evertune | ~$3,000+/mo | Thin review base | Large brands, API-scale rigor |
| Goodie AI | $399/mo+ | Thin review base | Monitoring plus content in one workspace |
| Gauge | $99-599/mo | PH 5.0/5 (3 reviews) | Affordable citation tracking |
| Mavel | €89-499/mo, custom above | No public reviews yet (new) | Narrative share, whose-frame, source consensus |
Every tool above, Profound included, does the same fundamental thing: it counts. Prompt in, answer out, brand mentioned or not, sentiment positive or negative. Profound’s Prompt Volumes data is genuinely strong (users call it “unmatched”), and its enterprise feature set is the deepest in the category. Peec scrapes real UI output so what you see matches what your customer sees. Gauge does citation tracking better than almost anyone, including Reddit citations most tools skip. These are real, differentiated strengths.
None of them explain why the model built the answer the way it did. They tell you the score. As one Peec review put it: “tells me the score but not how to improve it.” That’s not a Peec problem specifically. It’s a category-wide ceiling.
Why mentions don’t equal recommendation
Picture two SaaS brands in the same category. Brand A gets mentioned in 70% of AI answers about “best tools for X.” Brand B gets mentioned in 40%. A visibility dashboard says Brand A is winning.
But look closer. Brand B is the name the model reaches for first, unprompted, in the “if you want the best overall option” sentence. Brand A shows up mostly in “alternatives include” lists, the AI equivalent of a footnote. Brand B’s frame, funded, modern, built for scale, is the one the model has adopted as the default story about the category. Brand A is mentioned more and recommended less.
This happens because AI models don’t rank a database. They generate an answer from a learned narrative, the same way a person forms an opinion from years of scattered inputs rather than a spreadsheet. Mentions are a downstream symptom of that narrative. Fixing the symptom (get mentioned more) without touching the cause (which frame the model believes) is why brands plateau on visibility scores while competitors keep winning the actual recommendation.
The real metric that matters: narrative share
Narrative share asks a different question than any tracker above: whose frame is the answer built on? Not “did I appear,” but “whose story about this category did the model tell, and did it use mine?”
This is upstream of mentions and share-of-voice. It’s also harder to fake with more content, because it’s about which sources and framing the model has come to trust as consensus, not how many times your name appears in a scrape.
How to audit your narrative, not just your mentions
Start by running the prompts your buyers actually ask, not keywords, prompts: “best X for Y,” “X vs Z,” “is X worth it.” Then don’t just log whether you appear. Read the frame. What adjective does the model reach for to describe you versus your competitor? Which source is it clearly leaning on when it explains its pick? Is there a gap between how you want to be seen and how the model currently describes you?
This is where Mavel sits. It’s a newer, self-serve entrant (Starter at €89/mo, Pro at €249/mo, Growth at €499/mo, free GEO report to start), and it doesn’t have a public review base yet, so take that as it is. What it’s built to do differently is trace the narrative layer directly: Narrative Share, Perception Gap between how you want to be seen and how the model sees you, Source Intelligence on what’s driving the answer, and a prioritized what-to-ship instead of another score to stare at.
If you need enterprise-grade prompt volume data today, Profound is the strongest option. If you want the cheapest real entry point, Otterly.AI at $29/mo. If you want to see whose frame is actually winning your category and what to do about it, that’s the gap Mavel is built for.
Run a few prompts about your own category this week and read the frame, not just the name-drops. It’ll tell you more than any dashboard score will.
