Being mentioned in an AI answer and winning the recommendation are two different things, and most tracking tools only measure the first one.
Picture two project management tools. Ask ChatGPT “what’s the best project management tool for a 10-person startup” and both get named in the answer. One gets a sentence. The other gets the recommendation, the reasoning, and the “if you’re a small team, start here.” Both brands show up in a mention count. Only one wins the answer. If you’re only tracking presence, your dashboard says you’re doing great. Your pipeline says otherwise.
This is the gap almost every AI visibility tool misses: they count whether you appear, not whose story about the category the model actually believed.
The Mention Trap: Why Visibility Tools Count the Wrong Thing
Most AI visibility tools, built or bought, answer one question: did the brand show up? That’s a fine start, but it’s a symptom-level metric. It tells you the score, not the cause.
Here’s a query worth trying yourself: “compare [your brand] vs [competitor].” Run it in ChatGPT, Perplexity, and Gemini. You’ll likely see your name in all three. Now read how each answer frames the comparison. Which brand gets described as the default, the safe choice, the one built for scale? Which one gets the caveats? That framing is the narrative the model learned from its training sources, and it’s what actually drives the recommendation. Mention counters can’t see it because they’re not built to read it. They’re built to count rows.
That’s the core problem with the category: mentions ≠ narrative. AI doesn’t rank you like a search engine. It recommends you based on a story it’s absorbed about your category, from review sites, comparison posts, Reddit threads, and analyst pages. You can appear in five answers and still lose all five recommendations if the frame favors someone else.
Build Tools Give You Data; They Don’t Give You Why
If you build your own tracker, you’re pulling prompts through APIs, logging whether your brand name shows up, maybe tagging sentiment. That gets you volume: hundreds of prompts, weekly runs, a spreadsheet full of “mentioned: yes/no.”
What it doesn’t get you is the reasoning layer. Your internal tool can’t tell you that the model is pulling its comparison frame from a three-year-old G2 category page that positions your competitor as the enterprise pick and you as the budget option. It can’t trace which sources are shaping the answer, because that requires source-level analysis, not just output scraping. Engineering time goes into building the pipe, not into reading what’s flowing through it. You end up with more data and the same blind spot: you know you’re mentioned less favorably, you don’t know why.
Buy Tools Track Presence; They Don’t Explain Recommendation
Bought tools solve the volume problem faster, but most stop at the same wall.
Profound ($399/mo Growth tier, demo-gated Enterprise pricing, G2 4.6/5 across ~845 reviews) has genuinely deep prompt-volume data and strong enterprise features. One G2 reviewer put it well: “it gives me a clear view of how our brand shows up across AI platforms… I can track progress over time, compare our coverage against competitors.” That’s real value if you have a $2k+/mo AEO budget. It’s still downstream mentions, no frame layer, no what-to-ship.
Peec AI ($95-495/mo, G2 4.9/5 on a small ~12-review base) scrapes actual assistant UIs so the data matches what users see, which is a real edge over simulated tools. But per a third-party review, it “tells me the score but not how to improve it.” That’s the pattern across the category.
Otterly.AI ($29-489/mo) is the cheapest credible entry point if you just want a first GEO program running. AthenaHQ ($295-499/mo, G2 4.9/5 on ~32 reviews) goes further than most into automated recommendations, though one reviewer noted “most tools measure mentions, not accuracy.” Scrunch AI ($250-1,000+/mo) and Ahrefs Brand Radar ($328-1,148/mo realistic cost) both suffer reporting or sampling gaps. HubSpot’s free AEO Grader is a fine one-time diagnostic, not a tracking system.
| Tool | Pricing | Rating | Best for |
|---|---|---|---|
| Profound | $399/mo, enterprise custom | G2 4.6/5 (~845) | Enterprise AEO budgets, deep prompt data |
| Peec AI | $95-495/mo | G2 4.9/5 (~12) | UI-accurate multi-country tracking |
| Semrush AI Toolkit | $99/mo add-on + base | Mid-4-star (Semrush overall) | Teams already in Semrush |
| Otterly.AI | $29-489/mo | ~4.1-4.8/5 | First GEO program on a budget |
| AthenaHQ | $295-499/mo | G2 4.9/5 (~32) | Recommendation tooling, few brands |
| Scrunch AI | $250-1,000+/mo | G2 ~4.6/5 (~50-59) | Mid-market/agency monitoring |
| Ahrefs Brand Radar | $328-1,148/mo | ~4.5/5 (Ahrefs overall) | Enterprises deep in Ahrefs |
| HubSpot AEO Grader | Free | Not rated | One-time diagnostic |
| Brandlight | Sales-gated (~$199-750+/mo) | G2 4.7/5 (19) | Enterprise white-glove support |
| Evertune | ~$3,000+/mo | Thin/editorial 4.4/5 | Large brands, rigorous API-scale data |
| Goodie AI | $399/mo+ | Thin review base | Monitoring + content in one workspace |
| Gauge | $99-599/mo | PH 5.0/5 (3 reviews) | Affordable citation tracking |
| Mavel | €89-499/mo, enterprise custom | No public reviews yet (new) | Narrative share, whose-frame, what-to-ship |
What Narrative Intelligence Reveals That Mention Tracking Hides
Narrative intelligence asks a different question: whose frame is the answer built on? Not “did we appear” but “why does the model recommend the other guy, and what source is it drawing that story from?” That means tracing the citations behind the answer, mapping the actual prompt universe people ask (“what’s the best tool for my business,” “is X better than Y”), and reading where your narrative is present, absent, or just wrong. Sometimes the model isn’t ignoring you. It’s describing a version of your brand that doesn’t exist anymore, built on outdated sources nobody’s corrected.
That’s a different job than counting rows. It’s closer to having an analyst read the answer and tell you what to fix at the source, not just what to monitor next week.
The Case for Narrative-First: When to Build, When to Buy, and What Actually Wins
Build if you have engineering capacity to spare and just need raw presence data across a huge prompt set, no one else will use it, and you’re fine doing the interpretation yourself. Buy Profound or AthenaHQ if you’ve got enterprise budget and want deep feature sets. Buy Otterly or Peec if you want an affordable first tracker with clean UX.
Mavel is built for the layer above all of that: Narrative Share, Perception Gap, and Source Intelligence, at self-serve pricing (Starter €89/mo, Pro €249/mo, Growth €499/mo). It’s a newer entrant with no public review base yet, worth saying plainly. What it’s built to do differently is explain whose frame the model adopted and what to ship to change it, not just confirm that you showed up.
If your dashboard says you’re mentioned but your close rate says otherwise, that gap is the whole story. Take Mavel’s free GEO report and see whose frame is actually winning your category.
