Most AI visibility tools tell you whether you showed up in an answer. None of them, except one, tell you why the model recommended someone else.
The AI Visibility Trap: Mentions vs. Narrative
Type “best project management tool for agencies” into ChatGPT and watch what happens. It’ll probably name three or four brands, maybe with a sentence of reasoning attached. Now ask yourself: do you know why it picked those three and not you?
That’s the gap almost every AI visibility tool on the market leaves open. They’ll tell you that you appeared in 40% of relevant prompts last month, up from 32%. They won’t tell you that the model is quoting a G2 comparison article from 2024 that frames your category around a feature you don’t even lead with anymore. Mentions are the scoreboard. Narrative is the game being played underneath it.
Two brands can show up in the exact same AI answer. One gets a sentence: “also worth considering.” The other gets the recommendation, the reasoning, the “best for” tag. Same mention count. Completely different outcome. If your tool only counts appearances, it can’t tell you which one you are.
What Existing Platforms Measure (and Why It’s Incomplete)
Here’s a fast shortlist of what’s actually out there in 2026, with real pricing and ratings, so you can see the pattern for yourself.
| Tool | Pricing | Rating | Best for |
|---|---|---|---|
| Profound | Demo-led, historically $99-$2,000+/mo | G2 4.6/5 (~845) | 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, needs Semrush base | Mid-4-star (Semrush overall) | Teams already on Semrush |
| Otterly.AI | $29-$489/mo | ~4.1-4.8/5 (mixed sources) | Solo marketers, first GEO program |
| AthenaHQ | $295-$499/mo, credit-based | G2 4.9/5 (~32) | Funded startups wanting agents, not just tracking |
| Scrunch AI | $250-$1,000+/mo | G2 ~4.6/5 (~50-59) | Mid-market teams with their own execution plan |
| Ahrefs Brand Radar | $328-$1,148/mo realistic | ~4.5/5 (Ahrefs overall) | Enterprises already deep in Ahrefs |
| HubSpot AEO Grader | Free | Unrated (free tool) | A first diagnostic before buying anything |
| Brandlight | Sales-gated, ~$199-$750+/mo | G2 4.7/5 (19 verified) | Enterprise brand teams wanting white-glove |
| Evertune | ~$3,000+/mo | Trakkr editorial 4.4/5 | Large brands wanting rigorous API-scale data |
| Goodie AI | $399/mo self-serve | Thin review base | Monitoring plus content execution in one tool |
| Gauge | $99-$599/mo | PH 5.0/5 (3 reviews) | Citation tracking, incl. Reddit |
| Mavel | €89-€499/mo, custom above | No public reviews yet (new entrant) | Teams that want the narrative layer, not just mentions |
Every tool above the last row does some version of the same job: run prompts, scrape or query an engine, log whether your brand name showed up, maybe tag sentiment. That’s real, useful data. It’s also downstream. It tells you the symptom, not the cause.
How AI Models Actually Pick Brands: The Narrative Layer
AI search doesn’t rank pages. It recommends based on a story it has already learned about your category, assembled from articles, comparison posts, Reddit threads, review sites, docs, and whatever else got crawled and weighted into its training and retrieval process. When you ask “what’s the best CRM for a 10-person startup,” the model isn’t running a live tournament. It’s recalling a frame: who the “scrappy, affordable” player is, who’s the “enterprise-grade” one, who’s the “easy to set up” one. If your brand isn’t attached to a frame the model has already adopted, you don’t get recommended, you get listed.
That frame comes from sources. Consensus builds it. And consensus can be wrong, outdated, or quietly shaped by whoever published the loudest comparison content first. AI is downstream of that consensus, not a neutral judge of it.
The Platforms Reviewed and Their Blind Spot
Profound is the deepest enterprise option and its Prompt Volumes data is genuinely hard to match. It tells you what people are asking and whether you show up. It doesn’t tell you whose frame the answer used or why. Peec AI scrapes the real assistant UI, which matters for accuracy, but reviewers say it plainly: “tells me the score but not how to improve it.” AthenaHQ goes further than most into recommendations and automated agents, which puts it ahead on execution, but it’s still counting presence and accuracy against the mention layer, not the frame underneath it.
Scrunch AI has hallucination detection, a real differentiator, but it’s gated to Enterprise and reviewers still flag “minimal” recommendations. Ahrefs Brand Radar has massive scale behind it, and an independent test still found it undercounting ChatGPT mentions 3 to 123 against reality. None of this is a knock on these tools doing their job. It’s that their job stops at “were we mentioned,” and that’s a different question from “whose story is the model telling.”
Narrative Share: The Metric That Matters
This is the layer Mavel measures and nobody else on that list does: Narrative Share, whose frame the model actually adopted, upstream of mentions and share-of-voice. Alongside it: Perception Gap (how the model’s version of you differs from how you want to be seen), Source Intelligence (which sources are actually feeding the model’s answer), and Narrative Graph (how the frame moves across engines and time).
Mavel is self-serve starting at €89/mo, scaling to €499/mo for the full narrative stack, with custom Enterprise above that. It’s a newer entrant, no large public review base yet, so take that as it is. What it does differently isn’t in dispute: it explains why a model recommends who it recommends, traces the sources building that recommendation, and hands you a prioritized what-to-ship instead of a score to stare at.
How to Own Your Frame Before the Model Catches Up
AEO and GEO tactics, citations, structured data, prompt coverage, are real and worth doing. But they’re tactics sitting on top of a strategy question: what story does the model already believe about your category, and is it yours? If you’re only tracking mentions, you’ll optimize for appearing more often in a story someone else wrote. Fix the frame first. The mentions follow.
Want to see what this actually looks like instead of reading about it? Open a live example here: mavel.ai/analyze/sample. No signup, it’s a real sample narrative report you can click through right now.
