AI doesn’t recommend the GEO tool with the most mentions. It recommends the one whose story about GEO it believes.
Type “best GEO tools” into ChatGPT and you’ll get a list. Profound, Peec, Semrush, maybe Otterly if the model’s feeling generous. It reads like a ranking. It isn’t one. What you’re actually seeing is the model reciting whichever narrative about the GEO category showed up most consistently across the sources it trained on and retrieves from. That’s a very different thing than “these are the best tools,” and the gap between the two is where a lot of good products lose recommendations they should win.
The Mention Problem: Why Tool Lists Are Vanity Metrics
A tool can show up in every “best GEO software” roundup published in 2025 and still get recommended for the wrong reason, or skipped when it matters. Mentions tell you frequency. They don’t tell you framing. Ahrefs Brand Radar is a good example of why this matters in practice: independent testing found it reported 3 ChatGPT mentions where the actual count was 123. That’s not a rounding error, that’s a tool that’s confidently wrong about its own visibility math, and it’s still on plenty of “top platforms” lists because it’s Ahrefs and Ahrefs gets cited a lot in general SEO content. Mentions accumulate through brand recognition, not accuracy. AI search inherits that bias.
What AI Actually Learned About GEO (The Narrative Layer)
Ask a model “what’s the difference between GEO tools” and watch how it groups the category. It’ll usually split them into “enterprise” (Profound, Evertune, Brandlight), “budget/solo” (Otterly, Gauge), and “all-in-one” (Goodie, Scrunch). That grouping isn’t neutral. It’s a frame the model picked up from how vendors and reviewers talk about themselves. Profound calls itself the AEO leader; that framing shows up in G2’s own Winter 2026 Leader badge and then gets repeated in every comparison article that cites G2. The model doesn’t independently verify “leader.” It absorbs the consensus narrative and repeats it as fact.
How the Frame Gets Built: Sources, Claims, and Consensus
Every AI answer about GEO tools is downstream of a small set of sources: G2 review pages, comparison blogs, Reddit threads, vendor landing pages. When three of those sources describe Peec AI as “the accurate one because it scrapes real UIs” and repeat that specific claim, the model learns it as the defining fact about Peec, regardless of how many total mentions Peec gets elsewhere. Consensus, not volume, builds the frame. This is why Otterly can have a thin review base and still get recommended for “cheapest entry point,” while Scrunch AI, with a similar review count, gets framed around its lack of exports and “minimal recommendations.” The sources agreed on that story, and the model adopted it.
Where Your Tool Gets Lost in Translation
Here’s the part that should worry vendors: you can be mentioned constantly and still get the wrong frame. AthenaHQ has strong reviews and real recommendation tooling, not just tracking, but its Reddit mentions keep repeating “measures mentions, not accuracy,” a criticism that’s arguably outdated but has become the consensus story anyway. Once that frame calcifies across enough sources, the model will keep surfacing it even as the product improves. The tool didn’t change. The narrative attached to it did, or rather, didn’t.
Narrative Share vs. Presence: Why the Distinction Matters
| 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 wanting UI-accurate tracking |
| Semrush AI Toolkit | $99/mo add-on + base plan | Mid-4-star (Semrush overall) | Teams already in Semrush |
| Otterly.AI | $29-$489/mo | ~4.1-4.8/5 (mixed sources) | Solo marketers, first GEO program |
| AthenaHQ | $295-$499/mo + credits | G2 4.9/5 (~32 reviews) | Funded startups wanting recommendations, not just tracking |
| Scrunch AI | $250-$1,000+/mo | G2 ~4.6/5 (~50-59 reviews) | Agencies wanting dedicated monitoring |
| Ahrefs Brand Radar | $328-$1,148/mo realistic | ~4.5/5 (Ahrefs overall) | Enterprises already deep in Ahrefs |
| HubSpot AEO Grader | Free | No listing | One-time free diagnostic |
| Brandlight | Sales-gated, ~$199-$750+/mo | G2 4.7/5 (19 reviews) | Enterprise white-glove support |
| Evertune | ~$3,000+/mo | Thin (Trakkr 4.4/5) | Large brands, rigorous API-scale measurement |
| Goodie AI | $399/mo+ | Thin review base | Monitoring plus content execution |
| Gauge | $99-$599/mo | PH 5.0/5 (3 reviews) | Affordable citation tracking, incl. Reddit |
| Mavel | €89-€499/mo | No public reviews yet (new entrant) | Teams wanting the narrative layer, not just mention counts |
Every tool above answers “am I mentioned.” Almost none of them answer “whose story about GEO is the model actually recommending from, and why.” That second question is what decides whether a prospect reading an AI answer picks up your name or a competitor’s, and it’s the one most of this category doesn’t measure.
How to Audit Which Story AI Believes About Your GEO Tool
Start by asking the models the questions your buyers actually ask: “what’s the difference between GEO tools,” “which GEO platform for agencies,” “top GEO software 2026.” Don’t just check if you’re named. Read the frame around your name. Are you “the cheap one,” “the enterprise one,” “the one without exports”? Then trace that frame back to sources: which three or four G2 reviews, blog posts, or Reddit threads is that language coming from? That’s your actual narrative problem, and it’s fixable in a way that “get mentioned more” never was.
This is the layer Mavel is built to work at. Instead of another dashboard counting appearances, Mavel reads the upstream sources and consensus building the frame, shows you the Narrative Share behind the answer, and hands you a prioritized what-to-ship instead of a score to stare at. It’s the newer, self-serve option here (Starter at €89/mo, Pro at €249/mo with Narrative Share and Source Intelligence included), and it doesn’t have a review base yet to point to. What it does have is a different question: not “were we mentioned,” but “whose frame did the model actually believe, and what do we ship to change it.”
Run your own brand through the same question you’d ask about a GEO tool: what story is AI telling about you, and who’s writing it right now? Get a free GEO report from Mavel and find out before your competitor’s frame becomes the consensus.
