Category: LLM SEO

Keyword tweaks and schema markup are hygiene. What decides whether ChatGPT recommends you is the story your market already tells about your category.

If you’ve spent the last year optimizing for AI search, you’ve probably done all the right things and still watched a competitor get named first in ChatGPT. That’s not a bug in your execution. It’s a sign you’ve been working on the wrong layer. AEO and GEO are tactics. What actually moves an AI recommendation is narrative, and most of the advice out there confuses the two.

Here are the myths worth killing.

Myth 1: Optimizing for AI Search Means Rewriting Your Content for Keywords

AEO, or answer engine optimization, gets pitched as SEO with new keyword targets. Stuff the right phrases into your pages, add FAQ blocks, and the model finds you. The problem is that large language models don’t retrieve your category by matching strings. They’ve already learned a story about your space from thousands of sources, and they answer from that story.

Ask ChatGPT “what’s the best project management tool for agencies” and it won’t scan your homepage for the phrase “best project management tool for agencies.” It’ll pull from a compressed understanding of who’s known for what. If your content repeats the keyword but doesn’t shift what the market believes about you, you’ve added noise, not narrative.

Myth 2: Citation Count and Backlink Authority Determine Your AI Recommendation Share

Classic SEO logic says more authoritative links equal higher rank. People assume AI works the same way: get cited more, get recommended more. Citations do feed the model. But volume isn’t the lever. The frame those citations carry is.

Picture two competing CRMs. One has 500 mentions describing it as “cheap but limited.” The other has 150 mentions describing it as “the tool serious sales teams graduate to.” When a buyer asks which CRM to pick for a growing team, the second brand wins the recommendation despite fewer citations. The model adopted a frame that fits the question. This is why counting citations tells you almost nothing about whether you’ll be recommended.

Myth: More Mentions Mean More Recommendations

This is the belief baked into every visibility dashboard. Watch your mention count climb, assume your recommendation odds climb with it. They don’t move together. A model can name you in ten answers and recommend you in zero, because being mentioned and being chosen are separate jobs the answer performs.

Think about how a real ChatGPT response reads. Ask “which analytics tool should a Series A startup use,” and you’ll often get a paragraph that names four products. One is described as the one built for teams at that stage. Two get a sentence about being powerful but heavy. The last shows up as “also worth a look.” All four got mentioned. Your mention counter treats them as equal wins. The buyer reads it and picks the first one. The role you play in the sentence decides the outcome, not whether your name appeared.

More mentions can even work against you. If the extra volume all carries the “cheap but limited” frame, you’ve just reinforced the story that keeps you out of the serious recommendation. The model learns the description that shows up most consistently, not the one that shows up most times. This is what narrative share measures and mention share can’t: whose story the answer is built on, and which slot you’re filling inside it. Counting appearances tells you the model knows you exist. It doesn’t tell you the model recommends you.

Myth 3: Schema Markup and Technical AEO Will Get You Into Every AI Answer

Structured data helps machines parse your pages. It’s genuinely useful for hygiene. But schema describes your product. It doesn’t decide whether the model thinks your product is the right answer to a buyer’s question.

You can mark up every price, feature, and review star on your site and still be absent from the answer where it matters, because the model built its recommendation on a consensus that formed off your site entirely. In reviews, forum threads, comparison posts, analyst notes. Schema gets you readable. It doesn’t get you chosen.

Myth: Schema Markup Alone Wins AI Answers

It’s worth separating this from the general schema point, because a specific version of the myth keeps circulating: that if you get the JSON-LD perfect, the AI will treat your structured claims as truth and repeat them. It won’t. Schema tells the model what you say about yourself. The recommendation gets built from what everyone else says about you.

Imagine you mark up your pricing page to declare your product “enterprise-grade” with clean Product and Offer schema. Meanwhile, the top three comparison articles for your category call you “great for small teams, not ready for enterprise.” Ask Copilot or Perplexity who to pick for a 2,000-person rollout, and it sides with the comparison articles every time. Your markup was parsed. It just lost to the consensus. The model weighs a self-description against a chorus of third-party sources, and the chorus wins.

Schema does its real job: it makes your pages legible so you’re eligible to be pulled in. That’s table stakes. The mistake is treating eligibility as if it were persuasion. You can be perfectly legible and still be framed as the wrong choice. What changes the framing lives in the sources the model trusts about your category, and almost none of those are your own site. Fix the markup so the machine can read you. Then go work on the story it reads about you everywhere else.

Myth 4: If You Rank in Google, You’ll Rank in AI Overviews (and Vice Versa)

This is the one that stings, because it feels like it should be true. You’re #1 in Google for your main term and invisible in ChatGPT and Gemini. How?

Google ranking rewards page-level relevance and authority for a specific query. AI recommendation rewards whose story the model learned about the category. Those are different games. A competitor can dominate AI answers while sitting on page two of Google, because the market consensus describes them as the default choice even though their SEO is mediocre. Google AI Overviews itself blends both signals, which is why your Overviews presence often doesn’t match your blue-link position. Ranking is about the page. Getting recommended is about the frame.

Myth: LLM SEO Is Just SEO Rebranded

There’s a comfortable version of this whole topic that says nothing really changed. AI search is a new surface, sure, but the playbook is the same: publish good content, earn authority, win the query. Rename it GEO, keep doing SEO. That framing feels safe, and it’s why so many teams keep grinding tactics that don’t move the answer.

The overlap is real. You still need crawlable pages, credible sources, content that answers questions. But the object you’re optimizing changed underneath you. Classic SEO optimizes a page against a query. You compete for a slot on a results list, and the user chooses from ten links. LLM search skips the list. The model reads a learned story about your category and returns one synthesized recommendation, often naming a single brand as the default. There’s no page two to climb into. Either the model’s story puts you in the answer or it doesn’t.

That difference reshapes the work. In SEO you can win a keyword you don’t deserve by out-optimizing a page. In AI answers you can’t out-optimize the consensus. Ask Gemini “best help desk software for a small support team” and it isn’t ranking pages. It’s telling you what it learned the market believes, compressed into a recommendation. To change that, you have to change what the market says, and trace which sources the model actually drew from to build the answer. That’s a different discipline than title tags and internal links. Keep the SEO fundamentals. Just stop assuming they’re the same fight. They share tools; they don’t share the mechanism that decides who gets recommended.

Myth 5: Being Mentioned in an AI Answer Means You’re Winning the Narrative

Getting named feels like a win. But there’s a difference between appearing in an answer and being the answer. You can show up in a ChatGPT response as the “budget option” while a rival is described as the one built for teams like the buyer’s. Both of you got mentioned. Only one got recommended.

Mention share counts whether you appear. It says nothing about the role you play in the story. That’s the trap most visibility tools fall into: they report presence and call it progress.

The Real Lever: Narrative Share vs. Mention Share

Narrative share is whose frame the model adopts when it answers about your category. It sits upstream of mentions, upstream of citations, upstream of ranking. When the model decides which story about your space is true, everything downstream follows.

This is where Mavel works. We read the human-market consensus the model learned from, trace which sources carry it, and explain why the model recommends whom. You fix the cause instead of chasing the symptom. Most tools show you what AI says. We explain why it says it, so the output is a prioritized what-to-ship, not another dashboard you have to interpret.

How to Actually Move Your Position in AI Answers: From Tactics to Strategy

Keep the hygiene. Do your schema, your structured content, your technical AEO. Those keep you legible to the machines. Just stop expecting them to change the recommendation.

To move your position, start with the question the model is actually answering, find whose frame currently wins it, and identify the sources feeding that frame. Then ship the content, proof, and third-party signals that shift the consensus toward how you want to be seen. Not the version you invent. The one grounded in what’s true and observable. AEO and GEO are the tactics. Narrative is the strategy that decides which tactics even matter.

Want to see what this looks like on a real category? Open a live sample narrative audit at mavel.ai/analyze/sample, no signup, and see whose frame the model is actually building its answer on.

Related

• How LLM SEO Works: Why Tactics Without Narrative Don’t Move the Needle
• What Is LLM SEO? Why Tactics Without Narrative Don’t Move AI Recommendations

AEO and GEO get you into the answer. Narrative decides whether the model recommends you once you’re there.

What LLM SEO Actually Is (and Isn’t)

Ask around and you’ll get two definitions of LLM SEO. One says it’s about getting cited: structure your content so ChatGPT, Perplexity, and Google AI Overviews pull from your pages. The other says it’s about getting mentioned: appear in the answer when someone asks about your category.

Both are real. Both are also incomplete. Citation and mention are how you show up. Neither decides whether the model actually points a buyer at you.

Classic SEO ranked documents against a query. An LLM does something different. It reads a question, then answers from a learned story about your category. That story is the thing doing the recommending. If you optimize for appearance without touching the story, you’re polishing your spot in an answer that still sends the buyer somewhere else.

How LLMs Actually Read and Use Your Content

An LLM doesn’t read your page the way a human does. It doesn’t scan your homepage, feel persuaded, and decide to recommend you. Two separate things are happening, and they run on different clocks.

The first is training. The model learned about your category from a huge slice of the web scraped at some point in the past. Your marketing copy is a tiny drop in that pool. What dominates is how everyone else describes your space: the review roundups, the Reddit threads, the comparison blogs, the docs and forum answers where people talk about tools like yours without you in the room. The model compressed all of that into patterns. When someone asks a question, it reproduces those patterns.

The second is retrieval. Tools like Perplexity and Google AI Overviews fetch live sources at query time and summarize them. Here your content can matter directly, but only if it gets pulled into the fetched set, and only if it says something the model can lift cleanly.

So “getting read” splits into two jobs. Show up in the sources that get retrieved. Show up in the corpus the model already absorbed. Most teams obsess over the first and ignore the second, which is why they can win a citation and still lose the recommendation. The model reaches for your page, extracts a line, and drops it into a story it already decided on before it ever saw you.

Try this. Ask Perplexity “what’s the best analytics tool for a Shopify store,” then click through to the sources it cited. You’ll usually find the answer’s framing came from a couple of roundup articles, not from any vendor’s own site. The vendors got quoted. The roundups did the recommending.

Why Content Structure Decides What Gets Cited

Structure won’t fix your narrative. But bad structure will keep you out of answers you’d otherwise win, so it’s worth getting right.

Models and answer engines favor content they can extract without guessing. That means a clear question near a clear answer. A heading that matches how people actually ask (“How much does X cost?”) beats a clever one (“Pricing, reimagined”). A direct sentence that states the claim beats a paragraph that circles it. If a model has to infer what you mean, it’s more likely to grab a competitor’s page that just said it plainly.

A few things that consistently help:

• Answer the question in the first sentence under the heading. Don’t warm up. If the H2 asks “Is X good for small teams,” the next line should say yes or no and why.
• Use headings phrased as real prompts. Buyers and models both start from questions. Structure your page around the questions your category actually gets asked, not around your feature names.
• Keep claims self-contained. A sentence that only makes sense after three paragraphs of setup is hard to lift. A sentence that stands alone gets quoted.
• Make comparisons explicit and fair. Comparison content gets retrieved constantly. If your page lists real tradeoffs instead of a rigged scorecard, it reads as more trustworthy, and trustworthy sources get cited more.

Here’s the catch. All of this decides whether you get pulled in. It does nothing about how you get framed once you’re there. You can be the most extractable page on the internet and still get quoted as “a budget option some teams use,” because the framing lives in the sources around you, not in your schema markup. Structure is table stakes. It earns you a seat. It doesn’t tell the room what to think of you.

Tactics vs. Strategy: AEO/GEO Don’t Change What the Model Believes

AEO and GEO are insertion points. AEO gets your content shaped for answer engines. GEO gets you cited across the sources models draw on. Useful work, and you should do it.

But they operate on the surface. They change how you appear, not what the model believes about who wins in your category. You can nail schema, publish clean comparison pages, and get quoted in the exact articles the model reads. If the consensus story frames a competitor as the default and you as the scrappy alternative, that framing rides along into every answer.

This is the pillar most tools skip. AEO and GEO are tactics. Narrative is strategy. Tactics decide whether you’re in the room. Strategy decides who the room listens to.

How LLMs Learn What to Recommend (The Narrative Layer)

An LLM doesn’t hold opinions about your product. It absorbs the patterns in how the market talks about your category, then reproduces them.

When thousands of articles, forum threads, reviews, and docs describe “the best tool for X” a certain way, the model learns that description as consensus. So when a buyer asks “what should I use for X,” it answers from that consensus. Not from your homepage copy. From the aggregate story the internet already tells.

That’s why recommendation is downstream of narrative. The model learned a frame for your category. Whoever the frame positions as the obvious answer gets recommended, over and over, across phrasings of the same question.

Consensus: The Layer Above the Mechanics

Everything above this point is mechanics. Retrieval, structure, extraction, citation. Consensus is the layer that sits on top of all of it, and it’s the one that decides your fate.

Consensus is the story the market has settled into about your category. Who the default is. Who the specialists are. What the “safe” choice is versus the “risky” one. Which brands go together and which stand apart. No single article writes this. It emerges from thousands of them agreeing, mostly without coordinating. The model treats that agreement as truth, because from its point of view, that’s what truth looks like: the thing most sources say.

This is why two brands with identical citation counts get recommended at wildly different rates. The model isn’t tallying mentions. It’s asking, in effect, “what does the market believe about this space,” and answering from the consensus it learned. Your citations feed that consensus. They don’t override it.

Picture a category where, over three years, the same phrase attaches to one vendor across dozens of high-authority sources: “the enterprise-grade choice.” Meanwhile you get described as “great for startups.” Both descriptions might be genuinely positive. But when a mid-market buyer asks a model for a recommendation, the consensus routes them to the “enterprise-grade” brand, because that’s the frame the market built. You didn’t lose on features or on SEO. You lost on the story that formed above the mechanics.

The practical takeaway is uncomfortable. You can win every technical battle and still lose the war, because the war is fought at the level of what the market agrees is true. That agreement is measurable. It’s the frame the model adopts, the sources that reinforce it, and how your positioning drifts inside it over time. That’s the layer Mavel reads. Not whether you appear, but whose story the answer is built on.

The Narrative Share Problem: You Can Be Cited and Still Lose

Here’s the gap that visibility tools miss. Being cited is not the same as winning the answer. Mavel measures narrative share: whose story the model tells about your category, not just whether your name appears in it.

Picture the difference. You get mentioned in an answer as “another option worth considering.” A competitor gets mentioned as “the standard most teams start with.” Same mention count. Completely different outcome. One frame makes you a footnote. The other makes the sale.

Presence is countable. Framing is what moves the buyer. If your name shows up but the story around it puts someone else at the center, you’re paying for visibility that recommends your rival.

A Real Example: Two Brands, Same Visibility, Different Recommendations

Imagine two project management tools. Both are cited in the same set of articles. Both appear when you ask ChatGPT “what’s the best project tool for a small team.”

Brand A shows up phrased like this: “widely considered the default for teams that want simplicity.” Brand B shows up as: “a feature-rich alternative if you outgrow the basics.”

Identical presence. The model recommends Brand A anyway, because the frame it learned makes A the starting point and B the upgrade you consider later. B could publish twice the content and win every citation battle. Until the story shifts, the model keeps sending new teams to A.

That’s narrative share doing its work, quietly, underneath the metrics most people track.

The AEO Trap: Citation Optimization Without Frame Ownership

The trap is treating citation frequency as the finish line. Teams grind on getting quoted, watch the mention count climb, and wonder why pipeline doesn’t follow.

They optimized how they appear inside a story that positions someone else as the answer. It’s ranking number one for a query nobody trusts. The mechanics work. The outcome doesn’t move.

Citation without frame ownership is motion without direction. You need both, in order. Frame first, then the tactics that get you cited inside it.

What Actually Moves an LLM’s Answer (Narrative + Tactics)

The work is sequenced. First, understand the consensus story the model learned about your category and where your frame is winning or losing inside it. Then use AEO and GEO to reinforce the framing you want across the sources the model reads.

Tactics amplify a frame. They don’t create one. Get the story right and citation work compounds. Get it wrong and you’re louder inside a narrative that still recommends your competitor.

How to Audit Your Narrative Share Before You Optimize for Visibility

Start by reading the frame, not counting mentions. Ask the model the questions your buyers actually ask. “What’s the best tool for [your category]?” “Who should a small team use?” Watch not whether you appear, but how you’re positioned. Default or alternative. Safe choice or edge case. The one they name first or the one they hedge with.

Then trace the sources feeding that framing. The articles and threads the model leans on are where the story lives. Those inputs are where you change the output.

Open ChatGPT right now, ask it to recommend a tool in your category, and read whose frame the answer is built on. If it’s not yours, that’s the work. Mavel does this systematically. Free audit at mavel.ai/analyze.

Related

• What Is LLM SEO? Why Tactics Without Narrative Don’t Move AI Recommendations
• The Biggest LLM SEO Myths: Why Tactics Won’t Fix Your Narrative

LLM SEO gets you mentioned in AI answers. It doesn’t decide whether the model recommends you, because that’s set by the story it already learned about your category.

What Is LLM SEO (And Why the Term Misses the Point)

LLM SEO is the practice of optimizing your content so large language models pick you up when someone asks a question. People also call it AEO (answer engine optimization) or GEO (generative engine optimization). The playbook looks familiar if you’ve done search: structure your pages, answer real questions cleanly, earn citations from sources the models trust, keep your entity data consistent so ChatGPT and Perplexity and Google AI Overviews can parse who you are.

All of that helps you get mentioned. And getting mentioned is where most people stop thinking.

The term itself borrows the SEO mental model, where ranking is the game. But AI search doesn’t rank a list of ten blue links. It writes an answer. Inside that answer, a model decides which brand to name first, which to recommend, and which to leave out entirely. Those are narrative decisions, not ranking decisions. LLM SEO gets you into the room. It doesn’t decide what the model says about you once you’re there.

LLM SEO vs. Traditional SEO: What Actually Changes

Traditional SEO optimizes for a ranked list. You want to sit at the top of a page of links, and the searcher clicks through and forms their own opinion. Your job ends at the click. The user does the rest of the work: comparing, judging, deciding.

LLM SEO doesn’t work that way, because the model does the deciding for the user. Somebody asks Perplexity “what’s the best analytics tool for a Shopify store,” and they get one answer with two or three names in it. There’s no page of ten options to scroll. The model already synthesized the comparison, picked its favorites, and handed over a recommendation. The user never sees the sources you spent months earning unless the model chose to cite them.

A few things shift in practice.

Keywords give way to prompts. Nobody types “best crm small business 2025” into ChatGPT. They type “I run a two-person agency and need to stop losing leads in my inbox, what should I use.” The unit of demand is a full question with context, not a keyword string. That’s why mapping the prompt universe of your category matters more than a keyword list. The real questions buyers and models ask rarely look like search queries.

Rank position gives way to frame. In classic SEO, being #3 is measurably worse than being #1, and you fight to close that gap. In an AI answer, there’s no #3. There’s the tool that gets recommended and the tools that get name-checked in passing. Whether you’re the recommendation depends on the story the model learned, not on a position you can climb by adding internal links.

And attribution gets murkier. GSC tells you which query brought a click. When a model recommends you inside an answer, there’s often no click and no query string to trace. You’re left asking a different question. Not “what ranked” but “whose version of my category did the model repeat.” The old measurement stack, GA4 and GSC and rank trackers, wasn’t built to answer that.

The Narrative Layer: What AI Actually Reads Before It Mentions You

AI models don’t form opinions in a vacuum. They’re trained and grounded on what humans already wrote: review sites, comparison posts, Reddit threads, analyst notes, competitor content that frames your whole category. By the time a model answers a prompt, it’s working from a learned consensus about who does what and who’s best at it.

So when someone asks “how do AI models decide which brands to recommend,” the honest answer is that they recommend from a story they absorbed before your latest blog post existed. AI is downstream of that consensus.

This is why your brand can appear in AI answers but land in the weaker slot. The model isn’t confused about whether you exist. It’s confident about what you’re for, and its version might not match yours. It might describe you by a use case you’ve outgrown, or attach you to a price tier you left, or hand your strongest positioning to a competitor who told the story louder. Tactical AEO won’t surface any of that. It counts the mention and calls it a win.

Tactics vs. Strategy: Why AEO Wins Visibility but Loses Recommendations

Here’s the difference between SEO and AI optimization that nobody puts on the pricing page. Classic SEO fights over position on a results page. AI optimization is supposed to fight over the answer. But most AEO work still behaves like it’s chasing position: hit the schema, publish the FAQ, get cited, watch the mention count climb.

You can do every one of those things well and still lose the recommendation.

Picture two project management tools. Both are cited in the same AI answer. Both have clean pages and solid backlinks. Ask ChatGPT “what’s the best tool for a small remote team,” and one gets recommended while the other gets a polite mention as an “also available” option. Same visibility. Different outcome. The model learned that Brand A is the story for small remote teams and Brand B is the story for enterprise rollouts. No amount of schema changes that. The frame was set upstream, in how the market already describes each product.

That’s the gap. AEO and GEO are tactics. They move presence. Narrative is strategy, and it moves the recommendation.

Explain Why: How Narrative Share Decides the Recommendation

A dashboard that tracks mentions tells you that you appeared. It can’t tell you why the model chose someone else. That “why” is the whole game.

Mavel measures narrative share: whose frame the model adopts when it answers questions about your category. Not whether you got named, but whose story the answer is built on. When the model recommends a competitor, there’s a reason. A frame it adopted. Sources it leaned on. Mavel’s job is to explain that cause so you can fix it, instead of chasing the symptom by publishing more pages nobody asked for.

The reads that matter aren’t vanity counts. They’re where the market’s story about you diverges from how you want to be seen, and what’s driving the answer in the wrong direction. Fix the inputs that shape the output. That’s how you change a recommendation.

How to Tell If Your LLM SEO Is Working

Most teams answer this with a mention count. The number goes up, so the work must be working. But mention count can climb while your recommendation rate flatlines. You show up more often and still lose the same prompts to the same competitor. Presence rising and guidance staying flat is the clearest sign you’re measuring the wrong thing.

Ask better questions of your own work.

When the model names you, what does it say you’re for? Run a few real prompts through ChatGPT and Perplexity and read the sentences around your brand name. If a payroll platform built for startups keeps getting described as “a solid option for large enterprises,” you have a positioning problem no amount of citations will fix. The model absorbed a frame, and it’s the wrong one.

Are you the recommendation or the footnote? There’s a real difference between “the best tool for X is Brand A” and “other options include Brand A.” Watch which slot you land in across the prompts that matter to your buyers. That slot, not the raw mention, tracks with pipeline.

Whose sources is the model citing? If the answer about your category leans on a competitor’s comparison page or a review roundup that miscasts you, that’s the input shaping the output. Tracing the actual sources and citations behind the answer tells you where to act. A rising mention count tells you nothing about where the story came from.

Is the frame moving in your direction over time? Narrative drifts. The version of you the model repeats this quarter may not be the version it repeated last quarter. Watching that drift, and watching whether your own moves shift it, is how you know the strategy is landing. A single snapshot can’t show you that.

Where Most LLM SEO Efforts Stall

The common failure isn’t laziness. Teams are working hard. They’re just working on the layer that’s easiest to measure instead of the one that decides the outcome.

The first place things stall is treating volume as strategy. More pages, more FAQs, more schema markup, all shipped on the assumption that more inputs equal more recommendations. But if the market’s story about you is wrong, you’re just publishing more content into a frame that already miscasts you. You reinforce the misread instead of correcting it.

The second is optimizing your own site while the narrative lives elsewhere. You control your pages. You don’t control the Reddit thread, the analyst note, or the “top 10 tools” roundup that the model actually leans on when it builds an answer. Consensus forms across third-party sources, and a lot of it sits outside your CMS. Teams that only touch what they own keep hitting a ceiling and can’t figure out why.

The third is confusing appearing with winning. This is the one that traps good teams. Reporting says the brand shows up in ChatGPT, the slide looks great, and everyone moves on. Meanwhile the recommendation keeps going to someone else, and the pipeline quietly disagrees with the dashboard. Being mentioned in an AI answer is not the same as winning it.

And the last one is automation with no judgment attached. Tools that count presence can run themselves. Reading whose frame is actually winning takes interpretation. Narrative is interpretive, and pure automation misses it. That’s why Mavel pairs automated monitoring with human-grade intelligence: the judgment to read the story the market is telling and decide what to ship in response.

From Tactic to Strategy: Shipping the Frame Before the Model Catches Up

Keep doing AEO. Clean entity data and good content still matter as table stakes. But treat them as tactics inside a strategy, not the strategy itself.

The move is to own the frame before the model locks it in. Find the story your category is telling, see where you’re absent or miscast, and ship the specific content, positioning, and third-party proof that shifts the consensus in your direction. Then AI search learns the version you built on purpose. That’s an AI visibility strategy that actually moves the answer, not just the mention count.

If your reporting says you’re “showing up in ChatGPT” but your pipeline disagrees, the narrative layer is where the leak is. Mavel measures narrative share: whose story the model tells about your category, and what to ship to change it.

Related

• How LLM SEO Works: Why Tactics Without Narrative Don’t Move the Needle
• The Biggest LLM SEO Myths: Why Tactics Won’t Fix Your Narrative