Category: AI Visibility Tools

Being mentioned in an AI answer isn’t the same as winning it, and most visibility tools can’t tell you the difference.

The Mention Trap: Why Visibility Tools Measure the Wrong Thing

Picture two project management tools. Call them Brand A and Brand B. Someone asks ChatGPT “what’s the best project management software for a 20-person startup?” Brand A gets mentioned. Brand B gets mentioned too, third in the list, right after Brand A’s biggest rival.

Most AI visibility tools would call this a win for both brands. They showed up. They got counted. Someone’s dashboard turned green.

But here’s what that dashboard doesn’t show: the answer was built entirely on a competitor’s definition of “best for startups.” The model framed the category around speed and simplicity, a story the top-ranked brand has been feeding it for months through comparison posts, Reddit threads, and G2 reviews. Brand A benefits directly from that frame. Brand B just got swept in as an afterthought, mentioned but not chosen.

That’s the mention trap. Visibility tools count appearances. They don’t tell you whose story the model is actually repeating, or why it picked that story over yours. Counting mentions without understanding the frame behind them is like counting how many times your name comes up at a meeting you weren’t invited to help plan.

What Actually Drives AI Recommendations (Spoiler: Narrative, Not Presence)

AI search doesn’t rank the way a search engine ranks. It recommends, based on a narrative it’s learned about your category from the sources it trusts. Ask “why does ChatGPT recommend Competitor X over us,” and the honest answer usually isn’t “because Competitor X has more mentions.” It’s because Competitor X’s framing of the category has more consensus behind it. More sources describe the category the way Competitor X describes it. The model absorbed that framing and now reproduces it by default.

This is why presence and guidance are different things. You can be present in an answer and still lose the recommendation, because the model isn’t guided by your version of the story. It’s guided by whichever version showed up most consistently across the sources it draws from: comparison sites, review platforms, forums, docs, analyst posts. Google AI Overviews works the same way. It’s not picking a brand to feature first at random. It’s reflecting whichever frame has the most weight behind it in the sources it’s synthesizing.

So the real question was never “are we showing up.” It’s “whose definition of this category is the model treating as true, and is it ours.”

Four Signals It’s Time to Invest in Narrative Intelligence

Not every team needs this yet. But a few signals mean you’re past the point where a simple mention tracker will help you:

1. You’re mentioned, but not recommended first. Something more foundational than SEO is happening in how the model frames the category.
2. AI answers describe you inaccurately, or invent a version of your product that doesn’t exist. That’s not a bug you can patch with more content. It’s a sign the model never learned your actual frame.
3. A competitor with a smaller market share keeps outranking you in AI answers. They’ve likely won the narrative even though they haven’t won the market.
4. You’ve asked “why does AI recommend them and not us” and nobody on your team can answer with sources, only guesses. That’s the moment a mention counter stops being useful and you need something that explains the mechanism.

Mentions vs. Narrative Share: A Concrete Example

Try this yourself: ask Perplexity “what’s the best CRM for a small sales team” and read the full answer, not just the brand names. Notice how it frames the category. Is it framed around price? Ease of use? Integrations? Now ask what sources it’s pulling from. Often you’ll see a cluster of comparison sites and community threads that all repeat the same three or four adjectives about the same brand.

That repeated framing is narrative share. It’s not how often your name shows up. It’s whether the model’s whole mental model of “what this category is for” matches how you’d describe yourself. A brand mentioned once but embedded in the frame the model trusts will outperform a brand mentioned five times as an also-ran.

How to Measure What Matters Before You Buy

Before signing up for any AI visibility tool, ask what it actually measures. Most answer tracking tools will tell you: appeared in 40% of prompts this week, mentioned alongside three competitors, cited by these five sources. That’s useful as a baseline. It’s descriptive.

What it won’t tell you is why the model chose the frame it did, or whether your version of the story is even represented in the sources feeding these engines. If a tool can’t explain the reasoning behind a recommendation, at best you’ll know you have a visibility problem. You won’t know what to fix.

The Right AI Visibility Software for Your Stage

If you’re just starting to check whether your brand shows up in ChatGPT, Copilot, or Google AI Overviews at all, a basic tracker is a fine entry point. That’s the AI-visibility language most teams start with, and it’s legitimate.

But once you know you’re present and still losing the recommendation, once inaccurate answers and inherited competitor framing become the real problem, you need something built to read the narrative itself: the sources, the frame, the gap between how you’re described and how you want to be seen. That’s a different category of tool entirely, and it’s the one that actually moves the answer.

If you’re not sure which stage you’re at, that’s usually the first thing worth figuring out before you buy anything. Talk to Mavel, we’ll show you whether your problem is presence or narrative, and what to actually ship about it.

Related

• What Is AI Prompt Tracking Software? (And Why Counting Mentions Misses the Real Problem)
• Why ‘Best GEO Tools’ Misses the Real Question: Mentions vs. Narrative
• The Best AI Visibility Tools Aren’t Measuring What Matters

SEO tools measure rank. AI visibility tools measure mentions. Neither one tells you whose story the model actually believes about your category.

The core gap: SEO tools track rank, AI tools track mentions (both miss narrative)

Ask most marketing teams how they’re doing in AI search and they’ll show you two kinds of dashboards. The SEO dashboard says they rank #2 for “best project management software.” The AI visibility dashboard says they got mentioned in 34% of ChatGPT answers about the category last month. Both numbers look fine. Neither one explains why a prospect asked ChatGPT for a recommendation and got a competitor’s name first, with your brand tucked into a footnote as “also worth considering.”

That’s the gap. Rank and mentions are both downstream measurements. They tell you the AI noticed you. They don’t tell you whoseframing of the category the model is actually working from, or why it keeps reaching for a competitor’s story instead of yours. Mentions are a data point. Narrative is the thing that decides what happens to that data point once the model starts writing its answer.

Here’s the current toolset, so you know what each one actually does before you buy it.

Tool	Pricing	Rating	Best for
Profound	~$399/mo, enterprise $2k-5k+/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 multi-country tracking
Semrush AI Toolkit	$99/mo add-on (+base plan)	No dedicated listing	Teams already in Semrush
Otterly.AI	$29-489/mo	G2 ~4.8 (unconfirmed)	Solo marketers, first GEO project
AthenaHQ	$295-499/mo	G2 4.9/5 (~32 reviews)	Funded startups wanting recommendation tooling
Scrunch AI	$250-1,000+/mo	G2 4.6-4.7/5 (~50-59 reviews)	Agencies wanting dedicated monitoring
Ahrefs Brand Radar	$328-1,148/mo realistic	No dedicated listing	Enterprises deep in Ahrefs already
HubSpot AEO Grader	Free	No listing	One-time diagnostic before buying a tracker
Brandlight	Sales-gated (~$199-750+/mo)	G2 4.7/5 (19 reviews)	Enterprise brand teams, white-glove support
Evertune	~$3,000+/mo	Thin review base	Large brands, API-scale rigor
Goodie AI	$399/mo+	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	No public reviews yet (new)	Teams that want the narrative layer, not just mention counts

What SEO tools actually measure (and why it’s becoming insufficient)

Traditional SEO tools measure your position against a keyword and a ranking algorithm you can reverse-engineer with backlinks, content depth, and technical fixes. That model assumes a searcher types a query, gets ten blue links, and clicks one. AI search breaks that assumption. There’s no page of ten links. There’s one answer, synthesized from a model’s internal sense of what’s true about your category. Rank position doesn’t exist in that world. Being “#1 for CRM software” on Google means nothing to ChatGPT, which isn’t looking at your rank at all. It’s drawing on a learned narrative about CRM software and deciding which brands fit which parts of that story.

What AI visibility tools measure (and why it’s still incomplete)

Tools like Profound, Peec, Otterly, and Scrunch fixed the immediate problem: they tell you whether you show up in AI answers at all, across ChatGPT, Perplexity, Gemini, and Copilot. That’s genuinely useful, and Profound in particular has built serious depth here (its Prompt Volumes data is called “genuinely unmatched” by reviewers). But presence isn’t the same as preference. One AthenaHQ reviewer put it plainly: “Most tools measure mentions, not accuracy.” You can appear in an answer and still lose the recommendation. Counting appearances tells you the AI knows you exist. It doesn’t tell you why it keeps recommending someone else first.

Mentions vs. narrative: a real-world example

Try this. Ask ChatGPT “what’s the best CRM for a 20-person sales team” and read the answer closely. Notice which brand gets the confident, detailed pitch, complete with specific reasons (“built for fast-growing teams,” “simple onboarding”), and which brands get a one-line mention in a “you might also consider” list at the end. Both brands showed up. A mentions dashboard would count that as two visibility wins. But only one of them got the frame: the story the model is telling about what a 20-person sales team actually needs, and which brand fits that story. The other brand is present but invisible in the way that matters. That’s mentions without narrative.

Why AI recommends based on narrative, not presence

AI models don’t rank. They recommend from a learned consensus about your category, built from the sources they were trained on and the ones they retrieve at answer time. If the dominant sources describe your category through a competitor’s lens, the model will keep reaching for that lens even when your brand technically appears in the training data. Fixing that isn’t a content-volume problem. It’s a “whose frame wins” problem, and no amount of getting mentioned more often changes who’s writing the story.

How to read what AI actually sees about your brand

This is the layer most tools skip, and it’s the one Mavel was built for. Instead of stopping at “you were mentioned 34% of the time,” Mavel measures Narrative Share: whose frame the model actually adopted. It maps the Perception Gap between how you want to be seen and how the model currently describes you, traces Source Intelligence back to the actual citations shaping the answer, and turns all of it into a prioritized what-to-ship, not another score to stare at. Mavel is a newer entrant and says so plainly. It’s self-serve starting at €89/mo, built for agencies and funded SaaS teams who’ve already got the mentions dashboard and need the layer underneath it.

The question your next tool should answer: whose frame is winning?

Before you buy another visibility tracker, ask what it actually tells you when a competitor outranks you in an AI answer. If the answer is “they got mentioned more,” you’ve bought a counter. If the answer explains whose narrative the model is running on and what source is feeding it, you’ve bought something that can actually change the outcome.

Go ask your AI assistant a buying question in your category right now and read who gets the confident pitch versus the footnote. That’s the frame you’re up against. Mavel does this systematically: free audit at mavel.ai/analyze.

Related

• Why ‘Best GEO Tools’ Misses the Real Question: Mentions vs. Narrative
• What Features Should an AI Visibility Tool Have? (Spoiler: Tracking Mentions Isn’t Enough)
• What is AI Brand Intelligence Software? (It’s Not What You Think)

Most AI visibility tools tell you that you showed up in an AI answer. They don’t tell you why the model recommended someone else. Here’s the difference, and why it matters more than any presence score.

The Visibility Trap: Why Mentions Don’t Equal Influence

Try this: ask ChatGPT to recommend a project management tool for a 50-person startup. Then ask it why it picked the ones it picked. Most brands never get past the first question. They check whether they showed up, log the win, move on.

But being named isn’t the same as being trusted. A model can mention your brand in a list of five options and still frame you as the budget pick, the legacy option, or the one with the asterisk. Getting quoted is not the same as getting the story right.

This is the visibility trap. You track mentions because mentions are countable. Narrative is harder to count, so most tools skip it. That’s the gap AI visibility platforms exploit: they sell you a number that feels like progress but doesn’t explain outcomes.

If your competitor keeps getting recommended first in ChatGPT and you keep getting mentioned third or not at all, the fix isn’t “get mentioned more.” It’s understanding whose frame the model is running on. Mentions are the symptom. Frame is the cause.

What AI Visibility Platforms Actually Track (And Why It Misses the Real Game)

Most AEO and GEO tools on the market today, Profound, Peec, and similar players, do one thing well: they tell you if and where you show up across ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews. They run prompts, log citations, chart your presence over time. That’s useful. It’s also incomplete.

Here’s what they miss. AI search doesn’t rank you the way a search engine ranks pages. It recommends you based on a learned narrative about your category, built from the sources it trusts most. The model has already decided who the “innovative” player is, who’s “budget,” who’s “enterprise-grade,” and who’s an afterthought. Presence tracking shows you the output. It doesn’t touch the input that produced it.

So you can improve your mention rate and still lose. You show up more often, but always in the same frame: the safe alternative, the smaller competitor, the one people should also consider. That frame was set somewhere, by some sources, and no amount of counting mentions will tell you where.

Mentions ≠ Narrative: The Hidden Layer

Picture two SaaS companies in the same category. Company A gets mentioned in 80% of relevant AI answers. Company B gets mentioned in 50%. On a visibility dashboard, A wins clearly.

Now look at how each gets described. Company A shows up as “a lower-cost alternative for smaller teams.” Company B shows up as “the leading platform for mid-market companies scaling fast.” Which one do you think gets picked when the buyer is actually ready to choose?

That’s narrative share: whose story the model has adopted as the default account of your category. It sits upstream of mentions and share-of-voice. AI is downstream of consensus. It’s built its worldview from a pile of sources, reviews, comparison posts, analyst write-ups, Reddit threads, docs, and it’s repeating that worldview back as fact. If the consensus was built by your competitor’s content team, you’re funding your own bad frame every time you get quoted inside it.

That’s also why representation can be manipulated. Whoever seeds the most consistent, repeated framing across the sources models trust tends to win the frame, regardless of who’s actually better. Presence isn’t guidance. Being visible doesn’t mean the model is steering people toward you.

How to Evaluate an AI Visibility Platform: The Right Questions to Ask

Before you buy any AI visibility or AEO tool, ask these:

• Does it show mentions, or does it explain why the model recommends the brands it recommends?
• Does it identify the sources and citations feeding the model’s answer, or just the answer itself?
• Does it separate “we appeared” from “we were framed well”?
• Does it track drift, meaning how the narrative about your category shifts over time as new sources get published?
• Does it give you a prioritized list of what to fix, or just a dashboard you have to interpret yourself?

If a platform can’t answer the “why,” it’s a mention tracker wearing an AEO label. That’s fine if all you need is a presence report for a board slide. It won’t help you change the outcome.

Source Intelligence vs. Dashboards: What Separates Real Insight from Vanity Metrics

A dashboard tells you that you’re losing. Source intelligence tells you why and where. The difference is whether you’re staring at a score or looking at the actual sources, comparison sites, review platforms, forums, docs, that the model is pulling its frame from.

Once you know which sources are shaping the narrative, you know what to fix: which comparison page is misrepresenting you, which review site is outdated, which competitor’s content is quietly becoming the model’s default reference. That’s an action list. A visibility score is not.

Building Your AI Narrative Strategy (Beyond Presence)

AEO and GEO are tactics. Getting cited, structuring content for retrieval, optimizing for specific prompts, all of that matters. But it’s downstream work if you haven’t first figured out whose frame you’re fighting. Narrative is the strategy layer sitting above the tactics: define the story you want told, find where the current story diverges from that, and fix the sources causing the gap before the model locks the frame in further.

Mavel was built to sit at that layer. We measure Narrative Share, the Perception Gap between how you want to be seen and how AI actually describes you, and the source intelligence behind it, then hand you a prioritized list of what to ship, not another score to interpret.

Want to see what that actually looks like before you talk to anyone? Open a live sample report, no signup required: mavel.ai/analyze/sample.

Related

• Why ‘Best GEO Tools’ Misses the Real Question: Mentions vs. Narrative
• Best AI Share of Voice Tools: Why Mentions Aren’t Strategy
• Best AI Brand Monitoring Tools: Why Mentions Don’t Equal Narrative Share

Counting how often Perplexity mentions your brand tells you almost nothing about whether Perplexity’s story about your category is working for you or against you.

The Mention Trap: Why “Tracking Tools” Miss the Real Game

Type “best Perplexity tracking tools” into any search bar and you’ll get a stack of articles listing platforms that check one thing: did your brand’s name show up in a Perplexity answer today, yes or no. Most of these tools were built for that single job, and they do it fine. But a mention count is a scoreboard with no explanation of the game.

Here’s the actual problem. You can appear in 50 Perplexity answers about your category and still be losing, because Perplexity mentioned you as an afterthought, a runner-up, or a brand with a caveat attached (“though some users report…”). Meanwhile a competitor gets named first, framed as the default choice, and the model builds its whole answer around their version of the category story. Both of you got “mentioned.” Only one of you won.

That’s the gap between tracking presence and understanding narrative. Presence tells you that you appeared. Narrative tells you why the model chose to frame you the way it did, and whose story it’s actually repeating. If you only ever look at the mention count, you’ll optimize for showing up more, not for changing what gets said about you when you do.

What You Actually Need to See in Perplexity Answers (Hint: It’s Not a Mention Count)

If you’re asking “how do I track my brand mentions in Perplexity,” you’re asking the wrong first question. The better ones:

• What frame does Perplexity use when it talks about my category? (Default recommendation, budget option, legacy player, niche tool?)
• Why does it recommend my competitor first, and what is that recommendation built on?
• Where is my representation wrong, outdated, or just missing entirely?
• What sources is Perplexity actually citing to build that answer, and do those sources agree with each other?

Try this yourself: ask Perplexity “what’s the best CRM for a 20-person startup” and read past the list. Notice which brand gets the confident, unhedged recommendation and which gets a qualifier. That qualifier is the tell. It didn’t happen because that brand wasn’t “tracked” well. It happened because the consensus sources Perplexity pulled from told a specific story, and nobody on that brand’s team was managing which story got told.

Source Intelligence vs. Presence Dashboards: The Difference That Matters

Here’s a comparison of the tools people actually use to watch AI answers, including where each one sits on the mentions-versus-narrative spectrum.

Tool	Pricing	Rating	Best for
Profound	Demo-led, historically $399-5,000+/mo	G2 4.6/5 (~845 reviews)	Enterprise AEO teams needing deep prompt-volume data
Peec AI	$95-495/mo, 3 engines	G2 4.9/5 (~12 reviews)	European SMBs wanting UI-accurate scraped data
Semrush AI Toolkit	$99/mo add-on + base plan	No dedicated listing	Teams already in Semrush wanting AI data alongside SEO
Otterly.AI	$29-489/mo	G2 ~4.8/5	Solo marketers, first GEO program on a budget
AthenaHQ	$95-499/mo, credit-based	G2 4.9/5 (~32 reviews)	Funded startups wanting recommendation tooling, not just tracking
Scrunch AI	$250-1,000+/mo	G2 ~4.6/5 (~50 reviews)	Agencies wanting hallucination detection and citation depth
Ahrefs Brand Radar	$328-1,148/mo realistic	No dedicated listing	Enterprises already deep in Ahrefs infrastructure
HubSpot AEO Grader	Free	No listing (free tool)	A quick one-time diagnostic before buying anything
Brandlight	Sales-gated, ~$199-750/mo	G2 4.7/5 (19 reviews)	Enterprise brand teams wanting white-glove support
Evertune	~$3,000+/mo	Gartner Representative Vendor	Large brands wanting API-scale rigor and media activation
Goodie AI	$399/mo self-serve	G2 ~4.9/5 (thin base)	Mid-market teams wanting monitoring plus content execution
Gauge	$99-599/mo	Product Hunt 5.0/5 (3 reviews)	Practitioners wanting best-in-class citation tracking
Mavel	€89-499/mo, custom above	No public reviews yet (new entrant)	Teams wanting the narrative layer: whose frame, source consensus, what-to-ship

Most of these tools, including strong ones like Otterly.AI for budget-conscious teams or Profound for enterprise prompt-volume data, are built to answer “did we show up.” Gauge stands out for citation tracking depth, and Peec AI users like that its data matches what real users see in the actual interface. Scrunch AI adds hallucination detection, which is closer to narrative work but gated to Enterprise plans. AthenaHQ is one of the few that ships actual recommendations instead of just a dashboard.

But none of them, on their own, tell you whose story Perplexity is repeating and why. That’s a different question, and it requires reading the sources behind the answer, not just the answer itself.

How to Read the Narrative Behind Perplexity’s Recommendation

When Perplexity recommends a competitor over you, it’s not doing independent judgment. It’s pattern-matching against a consensus built from articles, reviews, comparison pages, and forum threads it has learned to trust for that category. If three of the top five sources it cites for “best project management software” all repeat the same “great for enterprise, weak for small teams” line about your product, that’s not a mention problem. That’s a narrative problem, and it’ll show up the same way whether you’re asked about in ChatGPT, Gemini, or Copilot.

This is why source intelligence matters more than a presence score. A dashboard tells you Perplexity mentioned you 12 times last week. Source intelligence tells you which three sites are shaping that mention, what story they’re telling, and whether it’s even accurate anymore.

Building Your Narrative Layer: From Tracking to Action

Most tools stop at “here’s what AI said.” The useful next step is “here’s why it said that, and here’s what to ship to change it.” That’s a different job than tracking, and it’s the one most Perplexity monitoring tools weren’t built for.

Mavel was built specifically for that job. Instead of a mention count, it reads Narrative Share (whose frame the model actually adopted), the Perception Gap between how you want to be seen and how the model currently describes you, and the source consensus driving the answer, then turns that into a prioritized list of what to ship. It’s a newer, self-serve tool without a big public review base yet, so we’ll say that plainly. But if you’re past “are we mentioned” and into “why do they get recommended over us, and what do we fix,” that’s the layer it’s built for.

Pull up Perplexity right now and ask it the question your buyers actually ask. Read past your name to what it’s doing around your name. That’s where the real work starts, and that’s what Mavel’s free GEO report will show you.

Related

• SEO Tools vs. AI Visibility Tools: Why Mentions Don’t Equal Narrative
• What is AI Search Analytics Software? (And Why Your Mentions Don’t Mean You’re Winning)
• What Are AI Visibility Tools? (And Why They Miss What Actually Matters)

Most ChatGPT tracking tools tell you how often you show up. None of them tell you why the model picked the story it’s telling about your category, which is the part that actually decides who gets recommended.

The Tracking Tool Trap: Why ‘Mentions’ Isn’t Strategy

Type “best project management software” into ChatGPT ten times and you’ll get roughly the same three or four names, in different orders, with different phrasing. A tracking tool will happily tell you that you appeared in 6 of those 10 answers, and your competitor appeared in 9. That’s a real number. It’s also almost useless on its own.

Here’s what it doesn’t tell you: why the model keeps reaching for your competitor first. Whether it’s pulling from a G2 category page, a Reddit thread from 2023, or a comparison article your competitor’s agency planted eighteen months ago. Whether the story ChatGPT tells about your category even matches how you’d describe your own product. Counting mentions treats AI search like a scoreboard. But AI search doesn’t rank you, it recommends you, based on a narrative it’s learned about your category. Mentions are the output. The narrative is the cause.

Most of the market right now is built to measure the output.

What Popular ChatGPT Tracking Tools Actually Do (And Don’t)

Tool	Pricing	Rating	Best for
Profound	~$399/mo (Growth), enterprise $2k-5k+/mo, demo-gated	G2 4.6/5, ~845 reviews	Enterprise brands needing deep AEO feature set
Peec AI	$95-495/mo, 7-day trial	G2 4.9/5, ~12 reviews	European SMBs/agencies wanting UI-accurate scraping
Semrush AI Toolkit	$99/mo add-on + base plan	No dedicated listing	Teams already living in Semrush
Otterly.AI	$29-489/mo, free trial	G2 ~4.8/5 cited	Solo marketers/small agencies on a budget
AthenaHQ	$295-499/mo, credit-based	G2 4.9/5, ~32 reviews	Startups wanting recommendation tooling, not just tracking
Scrunch AI	$250-1,000+/mo, 7-day trial	G2 ~4.6-4.7/5, ~50-59 reviews	Mid-market teams bringing their own execution plan
Ahrefs Brand Radar	$328-1,148/mo realistic all-in	No dedicated listing	Enterprises already deep in Ahrefs
HubSpot AEO Grader	Free	No listing (free tool)	A quick one-time diagnostic
Brandlight	Sales-gated, ~$199-750+/mo	G2 4.7/5, 19 reviews	Enterprise teams wanting white-glove support
Evertune	~$3,000+/mo, demo-led	Gartner Representative Vendor	Large brands needing rigorous API-scale measurement
Goodie AI	$399/mo self-serve	G2 ~4.9 cited, thin base	Mid-market wanting monitoring plus content execution
Gauge	$99-599/mo	Product Hunt 5.0/5 (3 reviews)	Agencies wanting affordable citation tracking
Mavel	€89-499/mo, free GEO report to start	No public review base yet (new entrant)	Teams wanting the narrative layer, not just mention counts

Look at the pattern. Profound gives you enterprise-grade prompt volume data and competitor benchmarking, and one G2 reviewer says it’s “like having an assistant always keeping me in the loop on our AEO and LLM visibility performance.” That’s genuinely useful if you’re an enterprise team with budget. Peec AI scrapes the real assistant UI so what you see matches what your customers see, but as one buyer put it, it “tells me the score but not how to improve it.” Ahrefs Brand Radar has a huge data engine behind it, but an independent test found it reported 3 ChatGPT mentions where 123 actually existed. Otterly.AI is a great $29 entry point if you’re just starting a GEO program. AthenaHQ is one of the few that ships actual recommendations, not just monitoring.

Every one of these tools, at every price point, is answering the same question: are you showing up? None of them answer why the model is telling the story it’s telling.

Mentions ≠ Narrative: A Real Example

Picture two project management tools. One gets mentioned in ChatGPT answers 47 times a month. The other gets mentioned 12 times. On a mentions dashboard, the first one is winning by a mile.

But look closer. The brand with 47 mentions shows up buried in listicles, “here are 10 tools to consider,” rarely first, rarely with a clear reason attached. The brand with 12 mentions shows up less often, but when it does, ChatGPT says something specific: “best for distributed teams that need async approvals.” That’s a frame. That’s a story the model has learned to associate with that brand from somewhere, a review site, a comparison post, a category report, that’s built enough consensus for the model to repeat it as fact.

Twelve confident, specific mentions built on a clear frame will move more buyers than 47 generic ones. A mentions dashboard can’t see that difference. It just counts.

What You Actually Need to Track: Sources, Frames, and Consensus

If mentions are the symptom, the real diagnostic questions are upstream:

• What frame has the model adopted about your category? Is it telling a story where you’re the budget option, the enterprise pick, the outdated one? You may not agree with that story, but if it’s what the model repeats, it’s the story that’s shaping recommendations.
• Which sources is it actually drawing from? A citation to a stale 2022 roundup carries different weight than a citation to a current, detailed comparison. Most tracking tools show that a citation exists. Few show which sources are doing the real work of building consensus.
• Where is the gap between how you want to be seen and how the model actually describes you? That gap is often the highest-leverage thing to fix, and it’s invisible if you’re only counting appearances.

This is closer to reading a market than running a dashboard. It takes some human judgment: automated tools are good at detecting that something changed, less good at explaining what story is winning and why.

From Visibility to Narrative Share: A Better Approach

This is the gap Mavel is built for. Instead of just counting whether you show up, Mavel reads the upstream narrative: whose frame the model has adopted about your category, what sources built that consensus, and where the gap sits between how you want to be seen and how AI actually describes you. The output isn’t another score to interpret. It’s a prioritized list of what to ship to change the story, not just chase the number.

Mavel is newer and doesn’t have the review history Profound or Peec have built up. That’s an honest tradeoff. If you need enterprise-grade prompt volume data today, Profound is the safer bet. If you want the narrative layer, the whose-frame-and-why behind the mentions, at self-serve pricing starting at €89/mo, Mavel is built specifically for that gap.

Start by asking ChatGPT what it says about your category, and notice whether it’s just naming names or actually telling a story. If it’s a story, you need to know whose it is. That’s what we help you figure out, and what to do about it.

Related

• What Are the Best LLM SEO Tools? (Why AI Picks Them, Not You)
• When Should You Invest in AI Visibility Software? (Spoiler: It’s Not About Getting Mentioned)
• What is AI Brand Intelligence Software? (It’s Not What You Think)

Being mentioned in an AI answer and winning that answer are two different games, and most monitoring tools only play one of them.

The Mention Trap: Why Visibility Tools Measure the Wrong Thing

Ask ChatGPT for the best project management software and it might name Asana, Monday, and ClickUp in the same breath. All three got mentioned. All three show up on a dashboard as “visible.” But only one of them is the answer the model reaches for first, the one it frames as the obvious pick before hedging with the other two as alternatives.

That distinction never shows up in a mention count. Most AI search monitoring tools were built to answer one question: did my brand appear? That’s a fine start. It’s also where the analysis stops for almost every tool on the market right now. You get a checkmark. You don’t get an explanation.

The problem is that a checkmark tells you nothing about position, trust, or framing. It treats a brand mentioned as a footnote the same as a brand recommended as the default. Those are not the same outcome, and treating them as equal is exactly why so many teams stare at rising “visibility scores” while their actual recommendation rate barely moves.

What AI Search Monitoring Tools Actually Track (and What They Miss)

The current wave of tools, Profound, Peec, and similar platforms, do one job well: they tell you whether your brand shows up across ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews for a set of prompts. That’s useful data. It’s also a downstream symptom, not a diagnosis.

Here’s what they typically track:

• Mention frequency across models and prompts
• Share of voice compared to competitors
• Basic sentiment (positive, neutral, negative)
• Citation counts from certain sources

Here’s what they don’t track:

• Whether the model treats you as the primary recommendation or a hedge
• Which sources actually shaped the model’s framing of your category
• Why the model describes your product one way and a competitor’s another
• Whether the model’s version of you is even accurate

A tool can tell you that you appeared in 40% of prompts about CRM software. It can’t tell you that in most of those appearances, the model frames you as “good for small teams” while your competitor gets framed as “the enterprise standard.” That gap is the whole game, and it’s invisible to a system that only counts appearances.

The Narrative Layer: Why Some Mentions Win and Others Lose

AI search doesn’t rank brands the way Google ranks pages. It recommends them based on a story it has learned about your category, assembled from the sources it trusts most. That story decides who gets framed as the default, who gets framed as a niche option, and who gets left out of the frame entirely even while technically getting mentioned.

Picture two SaaS brands in the same answer to “best AI writing tool for marketing teams.” Brand A gets described as “widely used by marketing teams for its templates and integrations.” Brand B gets a single line: “also worth considering for smaller budgets.” Both got mentioned. Both would show up identically on a presence-tracking dashboard. But Brand A is winning the narrative and Brand B is losing it, and no amount of mention tracking will show you that difference or explain why it exists.

The why matters more than the what. Maybe Brand A’s category page consistently gets cited by the review sites the model trusts. Maybe Brand B’s own site describes a positioning the model has decided not to believe. You can’t fix either problem without knowing which sources built the frame in the first place.

Mentions ≠ Narrative: A Real-World Example from B2B SaaS

Try this yourself: ask ChatGPT “what’s the best AI search visibility tool” and then ask “what does Profound do differently from Peec.” You’ll likely get both brands mentioned in the first answer. But the second answer reveals framing: one tool might get described as “the market leader for enterprise AI monitoring,” the other as “a lighter alternative.” That framing didn’t come from nowhere. It came from a pattern of sources, reviews, comparison posts, and public narrative that the model absorbed and now repeats as fact.

If you only tracked mentions, you’d conclude both brands are equally visible. If you traced the narrative, you’d see one brand owns the frame and the other is renting space inside it.

From Symptom Watching to Cause Intelligence: What to Actually Monitor

Instead of asking “did I get mentioned,” the better questions are: whose frame did the model adopt for my category, what sources fed that frame, and where does the model’s version of my brand diverge from how I actually want to be seen. That’s the shift from watching a symptom to understanding a cause.

This means monitoring the prompt universe your buyers actually use, not just a handful of keyword-style queries. It means tracing which sources the model cites and weights most heavily. It means comparing the model’s description of you against your own positioning to find the gap. Mention counts can’t do any of that. They can only tell you the scoreboard, not why the score looks the way it does.

The Mavel Difference: Narrative Share, Not Presence Share

Mavel starts from a different question than presence tools do. Instead of counting whether you show up, we read whose frame the model is actually using, what sources built that frame, and where the model’s version of you drifts from reality. We call the resulting metric Narrative Share: not whether you’re in the answer, but whether your story is the one the model is telling.

That’s paired with Explain-why, so you’re not left guessing at the cause behind a score. You get the sources, the frame, and a prioritized list of what to ship to shift it. Presence tools hand you a scoreboard. Mavel hands you the reason the game is going the way it is, and what to do about it.

If you’re tired of watching a mention count go up while your actual recommendation rate stays flat, that’s the gap we built Mavel to close. Come see what your narrative share actually looks like.

Related

• What is AI Search Analytics Software? (And Why Your Mentions Don’t Mean You’re Winning)
• When Should You Invest in AI Visibility Software? (Spoiler: It’s Not About Getting Mentioned)
• How to Choose an AI Visibility Platform: Mentions vs. Narrative

Every LLM SEO tool will show you if you’re mentioned. None of them will tell you why the AI recommended someone else instead.

The Mentions Trap: Why Being Listed Isn’t Winning

Type “best project management tools” into ChatGPT ten times this week. You’ll probably show up in six or seven of those answers. Feels good, right? Now ask yourself: does the AI describe you the way you’d describe yourself? Does it lead with the thing you’re actually best at, or does it borrow someone else’s framing and slot you in as an afterthought?

That gap is the whole problem with how most people shop for LLM SEO tools. They want a dashboard that counts appearances. Mentions, citations, share of voice: all downstream numbers. They tell you the score. They don’t tell you why the model built its answer the way it did, or whose version of “best” it’s actually repeating.

Here’s a scenario worth trying. Ask an AI model “what’s the best CRM for a small sales team” and then ask “what’s the best CRM for scaling startups.” If a competitor owns the frame for “scalable” and you own the frame for “simple,” you’ll be mentioned in both answers but recommended in only one. A visibility tracker will show you present in both threads and call it a win. It isn’t. You’re a citation, not the answer.

The LLM SEO Tools AI Actually Recommends (and Their Narrative Ownership)

Here’s a real shortlist, pricing and ratings as of mid-2026.

Tool	Pricing	Rating	Best for
Profound	Demo-led, ~$399-5,000+/mo	G2 4.6/5 (845 reviews)	Enterprise AEO budgets, prompt volume data
Peec AI	$95-495/mo	G2 4.9/5 (12 reviews)	EU SMBs/agencies, UI-accurate tracking
Semrush AI Toolkit	$99/mo add-on + base plan	Mixed, no dedicated listing	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 + credits	G2 4.9/5 (32 reviews)	Funded startups wanting recommendations, not just tracking
Scrunch AI	$250-1,000+/mo	G2 4.6-4.7/5 (~55 reviews)	Mid-market/agency teams, dedicated monitoring
Ahrefs Brand Radar	$328-1,148/mo realistic	No dedicated listing	Enterprises already deep in Ahrefs
HubSpot AEO Grader	Free	Not listed	One-time free diagnostic
Brandlight	Sales-gated, ~$199-750+/mo	G2 4.7/5 (19 reviews)	Enterprise, white-glove support
Evertune	~$3,000+/mo	No public review base	Large brands, rigorous methodology
Goodie AI	$399+/mo	Thin review base	Monitoring plus content execution
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)	Whose-frame-wins, source consensus, what-to-ship

Every one of these tools is legitimately good at what it does. Profound’s Prompt Volumes data is genuinely hard to replicate. Peec’s founders answer Slack messages personally, according to nearly every review they get. AthenaHQ ships recommendations, not just scores. Gauge tracks Reddit citations better than anyone. None of them, including Mavel today, claims to be everything. But almost all of them are built around the same core question: are you present. That’s a narrower question than the one that decides recommendations.

How AI Chose the “Best” (Spoiler: It’s Not Popularity)

AI models don’t rank tools by counting who’s mentioned most across the internet. They build an answer from a learned narrative: which sources they trust for this category, what pattern of language shows up when people describe “the best X,” and which brand’s story matches that pattern most closely.

That’s why a tool with 845 G2 reviews (Profound) and a tool with 12 (Peec) can both show up as “top AEO tools” in the same AI answer. Review count isn’t driving the recommendation. Consensus is: Profound’s frame is “enterprise-grade AEO infrastructure,” and enough sources repeat that frame that the model has locked it in. Peec’s frame is “accurate, transparent tracking for teams who got burned by opaque tools,” and it shows up in different prompts where that framing matches.

The model isn’t doing a popularity contest. It’s pattern-matching a story it’s already absorbed from the sources it trusts.

The Narrative Each Tool Owns (and Where Yours Is Missing)

Look at the pattern across this category. Profound owns “enterprise depth.” Otterly owns “cheap and easy entry point.” AthenaHQ owns “insights that turn into action.” Scrunch owns “fast setup, dedicated monitoring.” Ahrefs owns “scale, if you’re already in the ecosystem.” Evertune owns “rigor for big media budgets.”

Notice what’s missing from every single one of those frames: an answer to “why did the model say this about me, and what do I ship to change it.” Every tool in this list, including strong ones like AthenaHQ that go beyond pure tracking, is still operating downstream of the answer. They tell you what happened. None of them explain the upstream frame the model adopted, or trace it back to the sources actually shaping that frame.

That’s the gap Mavel sits in. Not “did you get mentioned in the AEO tools roundup,” but “whose story about AEO tools is the model repeating, and why.” Mavel measures Narrative Share (whose frame the model adopts, not just who’s cited), runs a Perception Gap read against how you actually want to be seen, and does Source Intelligence to trace which inputs are shaping the answer. It’s newer, self-serve pricing starts at €89/mo, and there’s no public review base yet, so take that as a straightforward tradeoff: less social proof, more of a different lens on the problem.

Reframing Your Position Before AI Locks It In

If you’re choosing a tracker today, pick based on budget and depth: Otterly for a cheap start, Profound or Evertune if you’ve got enterprise money and want the deepest data, AthenaHQ if you want recommendations instead of raw counts. All fair choices for the mentions question.

But mentions aren’t the whole game. If you want to know why the model tells the story it tells, and what you’d need to ship to change that story before a competitor’s frame hardens into consensus, that’s a different question. Most tools weren’t built to answer it.

Curious whose frame AI is actually using for your category? Grab Mavel’s free GEO report and see what the model’s really building its answer on.

Related

• What Are the Best ChatGPT Tracking Tools? (And Why They’re Missing the Real Problem)
• What is AI Search Analytics Software? (And Why Your Mentions Don’t Mean You’re Winning)
• What Is AI Visibility Reporting Software? (And Why Mentions Aren’t Enough)

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.

Related

• What is AI Brand Intelligence Software? (It’s Not What You Think)
• What Are AI Visibility Tools? (And Why They Miss What Actually Matters)
• Best AI Citation Tracking Tools: Why Mentions Don’t Equal Narrative Wins

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.

Related

• What Are the Best ChatGPT Tracking Tools? (And Why They’re Missing the Real Problem)
• What is AI Brand Intelligence Software? (It’s Not What You Think)
• Why ‘Best GEO Tools’ Misses the Real Question: Mentions vs. Narrative