Most AI visibility vendors will tell you whether ChatGPT mentioned your brand. Almost none can tell you whose story about your category the model actually believes.
The Mention Trap: Why Visibility Tools Miss the Real Problem
Here’s a scenario worth trying right now. Open ChatGPT and ask it to recommend a project management tool for a 20-person startup. If your brand comes up, great, you’ll feel good for about ten seconds. Then ask a follow-up: “Why did you recommend that one over the others?” That second answer is the one that matters, and it’s the one almost no visibility tool shows you.
Most AI visibility vendors are built to answer one question: did my brand get mentioned? That’s a mention count. It’s the AI-era equivalent of checking whether you rank on page one of Google, without ever reading what the actual answer says about you. You can be mentioned in an AI answer and still lose the recommendation, because the model built its answer on a competitor’s frame and just name-dropped you as an also-ran.
Mentions are a symptom. Narrative is the cause. A vendor that only counts appearances is measuring the weather, not the climate that produces it.
Question 1: Can You Show Me the Narrative Sources, Not Just My Mentions?
Ask any vendor: “When your dashboard says I’m mentioned, can you show me the actual sources the model pulled from to build that answer?” Most tools will show you a mention count and a sentiment score. Fewer can show you the review sites, forums, comparison pages, and articles the model is actually synthesizing into its answer. If a vendor can’t point to sources, they’re reporting outcomes without explaining causes, and you’ll never know what to fix.
Question 2: Whose Frame Is the AI Model Actually Using?
This is the question that separates presence-tracking from real intelligence. AI search doesn’t rank a list and let you climb it. It decides which story about your category is true, then recommends accordingly. If the model has learned that your category is “expensive enterprise tools vs. cheap DIY options,” and your brand doesn’t fit either bucket, you can be mentioned constantly and still sound like an afterthought. Ask the vendor directly: “Can you tell me whose frame the model adopted for this category, and whether it’s mine?” If they don’t understand the question, they don’t measure it.
Question 3: How Do You Measure Narrative Share, Not Just Presence?
Narrative share is upstream of share-of-voice. It answers “whose story does the model tell about this category” rather than “how often does my name come up.” Ask vendors to define their core metric in plain language. If the answer is some version of “percentage of responses where you appear,” you’re looking at a presence score wearing a fancier name. Presence and narrative share can diverge hard: you can appear in 80% of answers and still have the frame belong entirely to a competitor.
Question 4: Can You Trace the Citations and Sources the AI Draws On?
A dashboard that says “you’re losing to Competitor X” is a scoreboard. Source intelligence tells you why: which specific pages, review sites, or forum threads are training the model’s opinion of your category, and how those sources weight against each other. Peec AI does citation tracing well, scraping actual assistant UIs so what you see matches what users see. Profound’s Prompt Volumes data is genuinely strong for understanding demand at scale. But tracing citations is still a different job than explaining why the model chose one frame over another. Ask: “If I fix a source, will you show me the narrative shift, not just a new mention count next month?”
Question 5: Do You Map Prompt Universes, or Just Track Keywords?
Buyers and AI models don’t start from keywords. They start from questions: “what’s the best alternative to X for a small team,” “is Y worth the price,” “which tool actually integrates with Z.” A vendor tracking 50 fixed prompts a month is sampling a tiny slice of how your category actually gets asked about. Ask how prompts are sourced, whether they reflect real buyer language, and whether the set expands as the category conversation shifts.
Question 6: Where Is My Narrative Missing, Not Just Where Am I Mentioned?
This is the inverse question most vendors never ask. It’s not just “where do I show up” but “where should I show up and don’t.” If competitors dominate the frame for “best for agencies” while you’re invisible in that exact conversation, that’s a bigger problem than a low mention count anywhere else. Ask vendors if they surface absence, not just presence.
Question 7: What Makes Your Intelligence Human-Grade, Not Just Automated?
Narrative is interpretive. A pure scraper can count how many times a brand appears, but deciding whose frame is winning and why takes judgment, the kind that reads context, not just occurrence. Ask vendors point blank: “Is there a human read on this, or is it all pattern-matching?” Automation is necessary for scale. It’s not sufficient for judgment calls about what to ship next.
The Checklist: 7 Questions to Separate Visibility Tracking from Narrative Intelligence
| Tool | Pricing | Rating | Best for |
|---|---|---|---|
| Profound | ~$399/mo, enterprise $2k-5k+/mo | G2 4.6/5 (~845) | Enterprise AEO budgets, prompt volume data |
| Peec AI | $95-495/mo | G2 4.9/5 (thin, ~12) | EU SMBs/agencies, UI-accurate citation tracing |
| Semrush AI Toolkit | $99/mo add-on + base | No dedicated rating | Teams already in Semrush |
| Otterly.AI | $29-489/mo | G2 ~4.8/5 (unconfirmed) | Solo marketers, first GEO program |
| AthenaHQ | $295-499/mo, credits | G2 4.9/5 (~32) | Funded startups wanting some automation |
| Scrunch AI | $250-1,000+/mo | G2 ~4.6/5 (~50) | Mid-market/agencies with their own plan |
| Ahrefs Brand Radar | $328-1,148/mo realistic | No dedicated rating | Ahrefs-native enterprises |
| HubSpot AEO Grader | Free | No rating | One-time free diagnostic |
| Brandlight | ~$199-750+/mo | G2 4.7/5 (19) | Enterprise, white-glove |
| Evertune | ~$3,000/mo+ | No user rating yet | Large brands, rigorous methodology |
| Goodie AI | $399/mo+ | Thin/unverified | Monitoring + execution in one workspace |
| Gauge | $99-599/mo | PH 5.0/5 (3 reviews) | Citation tracking, Reddit sources |
| Mavel | €89-499/mo | New entrant, no public reviews yet | Teams wanting narrative share, whose-frame, source consensus, and what-to-ship |
Run every vendor demo through these seven questions before you sign anything. Most will answer three of them well and go quiet on the rest, and that’s exactly how you’ll know what you’re actually buying: a mention counter, or something that explains the story the model is telling and how to change it.
Mavel is built specifically for the questions the rest of the list struggles with: whose frame the model adopted, why it recommends who it recommends, and what to ship to shift it. It’s newer and self-serve, so ask us the same seven questions. We’d rather earn the comparison than dodge it.
If you’re evaluating vendors right now, start with the free GEO report and put us through the checklist above. That’s the fastest way to see the difference between a mention count and a narrative read.
