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Category: Playbooks & Use Cases

  • AI Visibility for Agencies: Own the Frame Before the Model Catches Up

    Chasing mentions in AI answers is a lagging strategy. The narrative that gets your brand mentioned was decided upstream, months before the model ever answered a prompt.

    Every agency managing brand presence in AI search is running the same play right now: track mentions, count citations, try to nudge the number up. It feels like progress because it’s measurable. But it’s measuring the wrong layer.

    By the time your brand shows up in a ChatGPT answer or an AI Overview, the model has already decided the story. It’s decided who the leader is, who the “budget option” is, who gets recommended for which use case. Mentions are just the visible output of a narrative decision that happened earlier, somewhere in the training data and the sources the model trusts. If you’re only tracking presence, you’re reading the scoreboard after the game’s been decided.

    Why presence ≠ visibility (the symptom vs. the cause)

    Ask ChatGPT “what’s the best project management tool for a 10-person startup” and you’ll get two or three names, usually with a clear favorite. That favorite isn’t chosen at random. It’s built from a frame: a story about who’s easy to set up, who scales, who’s “for small teams” versus “for enterprise.” That frame came from somewhere: review sites, comparison posts, Reddit threads, G2 categories, product-led content that got picked up and repeated until it became consensus.

    Presence tools will tell you your brand showed up in the answer, or didn’t. That’s a symptom. It tells you the score, not why you’re losing or winning. If your brand is absent, the real question isn’t “how do we get mentioned more.” It’s “whose frame is the model running on, and why isn’t ours in it.”

    Agencies that report mention counts to clients are reporting the temperature, not the diagnosis. Clients want to know why a competitor gets recommended and they don’t. Mention tracking can’t answer that question. It can only confirm the symptom exists.

    The three narrative layers agencies miss

    Most agency AI-visibility work stops at one layer: is the brand mentioned. There are three layers underneath that actually determine the outcome.

    Layer one: the frame. What story is the model telling about the category? “Best for ease of use,” “best for enterprise compliance,” “best for solo founders.” Every category has 3 to 5 competing frames, and the model has picked one as dominant.

    Layer two: the sources. Which sites, reviews, comparisons and forums is the model actually drawing from to build that frame? This is rarely the brand’s own website. It’s third-party consensus.

    Layer three: the drift. Frames shift over time as new content gets published and re-ingested. A brand that was “the expensive one” a year ago can become “the enterprise standard” if enough sources start repeating that framing. Nobody’s watching for that shift until it’s already locked in.

    Agencies that skip these layers are optimizing blind. They can push a client’s name into more answers without ever touching the reason the model prefers a competitor.

    Mapping your category’s prompt universe: where is your frame absent?

    Buyers don’t search “best CRM.” They ask “what CRM should a 20-person sales team use if they’re moving off spreadsheets.” That’s a prompt, not a keyword, and it comes with its own implied criteria.

    Every category has a prompt universe: the real range of questions buyers and AI systems ask, from broad (“what’s the best [category] for [use case]”) to comparative (“why do people recommend [competitor] over [brand]”) to skeptical (“is [brand] worth it for a small team”). Mapping that universe means running the actual prompts, not guessing at keywords, and checking where your frame shows up, where it’s missing, and where a competitor’s frame has taken the space you should own.

    This is where agencies can build a real audit for clients: not “we’re mentioned in 40% of answers” but “here’s the exact set of questions where your narrative is absent, and here’s whose frame is filling that gap instead.”

    Source intelligence: audit the sources the model actually draws on

    If the frame comes from sources, the fix has to start with sources. A dashboard that says “you’re losing” doesn’t tell you where to act. Source intelligence traces the specific citations, comparison pages, and forum threads the model is pulling from to build its answer about your category.

    Picture two competing SaaS tools in the same space. One gets recommended for “best value,” the other for “best support.” Trace the citations behind each answer and you’ll usually find a handful of repeated sources: a G2 comparison, a Reddit thread from 18 months ago, a blog post that got picked up by three other sites. That’s the actual input shaping the output. Fix the source, and the frame eventually shifts. Ignore it, and no amount of new content on the brand’s own site will move the needle, because the model isn’t citing the brand’s site. It’s citing everyone talking about the brand.

    The playbook: 5 moves to own the frame before the model updates

    1. Map the prompt universe for the category. Run the real questions buyers ask, not guessed keywords.
    2. Identify the dominant frame the model has adopted, and name it plainly: “budget pick,” “enterprise-safe,” “easiest onboarding.”
    3. Trace the sources feeding that frame. Find the 5 to 10 pieces of content actually doing the work.
    4. Find the gaps where your frame is absent and a competitor’s is filling the space uncontested.
    5. Ship source-level changes (new comparisons, corrected claims, updated third-party content) before the next training or indexing cycle locks the current frame in further.

    Models update. Indexes refresh. The frame you own today isn’t guaranteed tomorrow, and neither is the one you’re losing to. The agencies that treat this as a one-time audit will get outpaced by the ones treating it as ongoing narrative maintenance.

    Proof: tracking narrative share, not just mentions

    Imagine two SaaS brands in the same category, both showing up in AI answers at similar rates: call it 35% mention frequency each. Mention tracking says they’re neck and neck. But look at whose frame the model is actually using: one is described as “the reliable choice for teams that need compliance,” the other as “a decent option if you’re on a budget.” Same mention rate, completely different recommendation weight. The first brand is winning the category. The second is a footnote that happens to get named.

    That’s narrative share: whose story the model is telling, not just whether your name appears in it. It’s the metric that actually explains why a client is losing deals to a competitor even when both brands “show up” in AI search at similar rates. And it’s the number agencies need if they’re going to prove ROI on AI visibility work that goes beyond a mentions graph trending up and to the right.

    If you’re an agency trying to explain to a client why a competitor keeps getting recommended over them, mentions won’t get you there. You need to see the frame, the sources, and the gap. That’s what Mavel is built to show. Worth a look before your next client review.

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  • AI Visibility for SaaS Marketing Teams: Own the Frame Before the Model Catches Up

    AI models don’t rank your SaaS product, they recommend from a story they’ve already learned about your category, and most visibility tools never touch that story.

    Why Visibility Tracking Fails SaaS Teams

    Here’s what a typical Tuesday looks like for a SaaS marketing team right now. Someone runs your brand name through ChatGPT, screenshots the answer, drops it in Slack with a “we’re mentioned!” and calls it a win. Then someone else runs the same prompt a week later and you’re gone, replaced by two competitors you’ve never worried about before.

    That’s the whole problem with visibility tracking. It tells you whether your name showed up. It doesn’t tell you why it showed up, why it disappeared, or why the model described your product as “budget-friendly” when your pricing page says otherwise.

    Tools like Profound and Peec are built to count mentions and track presence across AI answers. That’s useful data. But it’s downstream data. It tells you the symptom, not the cause. If your SaaS company appears in some AI answers and not others, the mention count won’t explain why. The explanation lives one level up, in the narrative the model has already built about your category, and whether your positioning fits inside it.

    The Frame vs. the Mention: What AI Models Actually Optimize For

    AI search doesn’t rank a list of vendors and pick the best-matching keywords. It answers from a learned narrative: a story about what your category is, who the real players are, and what problem each one supposedly solves best. When someone asks Perplexity “what’s the best customer data platform for mid-market SaaS,” the model isn’t scanning for relevance. It’s recalling a frame it already holds, then filling in the vendor names that fit.

    That means two brands with identical feature sets can get wildly different treatment. One gets called “the enterprise standard.” The other gets called “a cheaper alternative to X.” Neither description came from a spec sheet. Both came from the sources the model was trained and grounded on: review sites, comparison posts, analyst write-ups, Reddit threads, G2 categories.

    If your team is only tracking whether you’re mentioned, you’re measuring the output and ignoring the input. The real question isn’t “did we show up?” It’s “whose frame did the model just repeat, and is it ours?”

    Map Your Category Narrative: The Playbook

    Step 1: Audit the Prompt Universe (Not Keywords)

    Buyers don’t type keywords into ChatGPT. They ask questions: “what’s the best alternative to Segment for a Series B startup,” “which CDP integrates easiest with Snowflake,” “is [competitor] worth it for a 50-person marketing team.” Build a real list of these prompts, the ones your buyers and their AI copilots actually ask, across every stage of the funnel. Keyword research tells you what people search. Prompt mapping tells you what story they’re trying to get answered.

    Step 2: Read the AI Answer as a Narrative, Not a List

    Don’t just log which brands appear. Read the actual language. Is your product described as a leader, a runner-up, a “good for small teams” afterthought? Are your differentiators showing up, or has the model flattened you into a generic category filler? This is qualitative work. It requires someone reading answers the way an analyst reads a market report, not a scraper counting logo appearances.

    Step 3: Trace the Sources: Where Does the Model Get Its Story?

    Every AI answer is built on something: a G2 comparison page, a Reddit thread from 2022, an analyst report, your own outdated blog post. Find those sources. This is the part most visibility dashboards skip entirely, and it’s the part that actually explains the answer instead of just describing it.

    Step 4: Identify Narrative Gaps and Wins

    Now compare. Where does the model’s story match how you want to be seen? Where’s the gap? Maybe you’re winning the “easiest to implement” frame but losing “enterprise-ready” to a competitor with a weaker product but better analyst coverage. That gap is your roadmap.

    Move Faster Than Consensus: Three Levers

    Lever 1: Reposition Your Category Definition (Control the Frame)

    If the model has decided your category is “CDPs for enterprise,” and you’re not enterprise, you’re fighting a frame you’ll never win. Sometimes the move is redefining the category itself in your content and PR, not competing inside someone else’s definition.

    Lever 2: Seed Authoritative Sources Before the Model Learns Them

    Once a frame hardens across enough high-authority sources, it’s expensive to unwind. Get ahead of it: place your framing in the comparison pages, analyst briefings, and community threads before the consensus locks in, not after.

    Lever 3: Own the Prompt Language Your Buyers Use

    If buyers ask “best alternative to Segment,” and your content never uses that phrase, you’re invisible to that exact query pattern. Match your content and outreach to the real prompt language, not the keyword list from three years ago.

    Measure What Matters: Narrative Share, Not Mention Count

    Mention count answers “were we there?” Narrative share answers “whose story won?” It’s the metric that captures whether the model’s frame favors you: your positioning, your differentiators, your category definition, not just your logo appearing somewhere in the answer. If you’re trying to prove AI visibility impacts pipeline, this is the number to bring your CMO: not “we appeared 40 times this month,” but “the model’s frame of our category shifted toward our positioning on these specific attributes.”

    Case Study: How a B2B Data Platform Shifted Its AI Narrative in 90 Days

    Picture a mid-market data platform that kept losing the “best for technical teams” frame to a competitor with worse documentation but louder Reddit presence. Instead of chasing more mentions, they mapped the prompt universe, found the Reddit threads and G2 categories driving the model’s story, corrected the record with better technical content in those exact spaces, and watched the model’s description shift from “simpler alternative” to “built for engineering teams” within a quarter. No new mentions needed. Same visibility, different frame.

    Operationalize Frame Ownership

    Stop treating AI visibility like a scoreboard you check once a month. Build the habit: map the prompts, read the narrative, trace the sources, ship the fix. Do it before your competitor’s frame hardens into consensus, because once the model “knows” who you are, it’s slow to unlearn it.

    Want to see whose frame is actually running your category right now? That’s exactly what Mavel is built to show you, come talk to us.

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  • How to Measure ROI of an AI Visibility Program (Before Your Competitors Own the Frame)

    Counting mentions tells you what already happened. Measuring narrative share tells you what’s about to.

    Most teams measure AI visibility the same way they measured SEO in 2015: track a metric, watch it climb, call it proof. But AI search doesn’t rank pages. It builds a story about your category, then recommends from that story. If you’re only counting how often you show up, you’re measuring the outcome of a decision the model already made, not the thing that made it.

    That’s the gap this article is here to close. Real ROI on an AI visibility program isn’t about appearing more. It’s about whether you shaped the frame the model learned before it started answering questions about your category at all.

    The Wrong Metric (Why Mention Count Isn’t ROI)

    Picture two project management tools. Brand A gets mentioned in 60% of ChatGPT answers about “best software for remote teams.” Brand B gets mentioned in 35%. On paper, Brand A is winning.

    But ask the follow-up: what does the model actually say about each one? If Brand A shows up as “a solid option, though some users find it clunky for smaller teams” and Brand B shows up as “the go-to choice for distributed teams that need lightweight setup,” Brand B is winning the part that matters. It owns the frame. Brand A owns the footnote.

    Mention count measures presence. It says nothing about whether the story attached to your name is the one you’d choose. Two brands can have identical visibility scores and completely different fates once a buyer reads past your name into the sentence describing you.

    This is why counting mentions and calling it ROI misleads teams into thinking they’re ahead when they’re actually just loud.

    The Leading Indicator: Narrative Share and Source Dominance

    Here’s the mechanism nobody’s tracking: AI models don’t generate opinions from nowhere. They synthesize an answer from the sources that carry the most consensus weight in the training and retrieval data for your category. Review sites, comparison posts, Reddit threads, analyst writeups, your competitors’ content that ranks well enough to get cited.

    Narrative share is whose version of that story the model adopts. Source dominance is what determines it before the answer ever gets generated.

    If three of the five sources an AI model pulls from when answering “best CRM for small teams” describe your competitor as “the affordable, easy-to-set-up option” and describe you as “powerful but expensive,” that framing is baked in before anyone asks a question. Mention tracking would show you present in the answer. It wouldn’t show you why the story’s not in your favor, or which sources to fix.

    Source dominance is the leading indicator. Mentions are the lagging one. By the time you’re counting mentions, the frame’s already set.

    How to Measure Frame Ownership (Before the Model Catches Up)

    Start by asking the model directly. Try prompting ChatGPT, Perplexity, and Google AI Overviews with the actual questions your buyers ask: “what’s the best [category] for [use case],” “how does [competitor] compare to [you].” Don’t just note if you appear. Read the sentence. What adjective does it use? What’s the reasoning it gives for recommending someone else?

    Then trace it back. Which sources does the answer echo? Are they the same three review sites and Reddit threads every time? That repetition is consensus forming, and it’s forming with or without your input.

    Frame ownership means your sources: your content, your placements, your third-party mentions, are carrying the interpretation you want before the model locks it in. You’re not chasing the answer. You’re shaping the inputs that produce it.

    The Three ROI Signals That Actually Matter

    1. Narrative Share. Whose frame does the model use when it describes your category? Track this across your core prompts, not just branded search.

    2. Source Intelligence. Which sources is the model citing or clearly drawing from, and is your narrative represented in them, or absent?

    3. Perception Gap. The distance between how you want to be described and how the model actually describes you. This gap is your prioritized fix list, not a vanity score.

    Together these tell you whether you’re building the story or reacting to someone else’s.

    Building Your Baseline: What to Track Starting Now

    Pick 15-20 real prompts your buyers use, not keywords. Run them monthly across ChatGPT, Perplexity, Copilot, and Google AI Overviews. Log the frame used for you and your top three competitors. Note the sources each answer seems to draw from. That’s your baseline. Everything after is drift, up or down.

    Case Study: A Category Leader Shifted From Mention Tracking to Source Intelligence, Here’s What Changed

    Imagine a funded SaaS brand that spent a year optimizing for mentions: guest posts, PR blasts, citation-building. Mentions rose. But the frame stayed stuck: “reliable but pricier.” When they shifted focus to the handful of sources actually shaping that sentence, updating comparison pages, correcting outdated claims on review sites, briefing analysts directly, the frame started moving within a quarter. Mentions didn’t spike. The story did.

    The Math: From Narrative Share to Revenue Impact

    Narrative share converts to pipeline the same way category positioning always has: buyers act on the frame they’re given. A prospect who reads “the expensive option” starts a sales call skeptical. One who reads “the go-to choice” starts ready to buy. Track close rates and deal velocity against the frame the model was giving at the time of first touch. That correlation is your ROI case, built from cause, not just count.

    Want to see whose frame the models are actually running with in your category, and what to fix first? That’s what Mavel’s built to show you.

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  • How to Brief Writers for AI-Citable Content: Own the Frame Before the Model Catches Up

    Most content briefs are written for a search engine that ranks pages. AI search doesn’t rank, it recommends, from a story it already believes about your category, so your brief needs to target the story, not the page.

    The Brief You’re Writing Wrong (Symptom: Citations Without Narrative Authority)

    Open ten content briefs from ten different marketing teams and you’ll find the same skeleton: target keyword, word count, competitor links to reference, a header outline pulled from “People Also Ask.” Maybe someone added a line about “optimizing for AI Overviews.” That’s the whole AI strategy.

    Here’s what that gets you: a piece that might get cited in a ChatGPT answer once, buried three sources down, contributing zero to how the model actually talks about your category. You show up. You don’t win anything.

    The symptom is citations without authority. Your brand gets name-checked in an AI answer, but the frame around that mention, the story the model tells about who’s best and why, still belongs to a competitor. You’re a footnote in someone else’s narrative. That’s not a content problem. That’s a briefing problem, and it starts before a single word gets written.

    Why AI Cites You (And Why It’s Not About Word Count)

    AI models don’t cite the longest article or the one with the most keywords stuffed into an H2. They cite sources that fit a frame they’ve already built. Ask ChatGPT “what’s the best project management tool for a 10-person startup” and it doesn’t evaluate every PM tool fresh. It pulls from a learned consensus, something closer to “Asana is for structure, Notion is for flexibility, Linear is for engineering teams,” and then it finds sources that confirm that frame.

    If your content doesn’t fit the frame the model already holds, it gets skipped, no matter how well-optimized it is. If it does fit, and adds specificity or evidence the model didn’t have, it gets pulled in. Word count, keyword density, schema markup: none of that decides this. The frame decides.

    This is why two brands can publish nearly identical comparison pages and get wildly different treatment from AI search. One reinforces a story the model already tells. The other contradicts it with no evidence, so the model quietly ignores it.

    Map the Narrative Frame Before You Write

    Before your writer opens a doc, someone needs to answer a different question than “what’s the target keyword.” The question is: what story does AI search currently tell about this category, and where does our brand sit inside it?

    Run the prompts a buyer would actually type: “best [category] for [use case],” “alternatives to [competitor],” “[competitor] vs [competitor].” Read the answers across ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews. Don’t just note if you’re mentioned. Note the frame: what’s the story being told, who’s cast as the leader, the challenger, the niche pick, and what sources seem to be feeding that story.

    Now you have a decision to make, and it’s the actual strategic choice your content brief needs to encode: reinforce the frame or shift it.

    Reinforcing means your content becomes better evidence for a story that already favors you (or is neutral toward you). Shifting means you’re deliberately introducing a counter-narrative, “actually, the real distinction in this category isn’t X vs Y, it’s Z,” and backing it with content strong enough that the model starts citing your version of the distinction instead of the old one.

    Skip this step and your writer is just guessing which story to feed.

    Five Elements of an AI-Native Content Brief

    1. The current frame. One paragraph: what story does AI search tell about this category right now, in plain language, based on actual model outputs you pulled.
    2. Reinforce or shift. State explicitly which move this piece makes and why. Don’t leave it implied.
    3. The sources currently winning. List what’s actually getting cited for the target prompts. Your writer needs to know what they’re competing with, not just who ranks on Google.
    4. The claim, stated plainly. Not a keyword. A sentence: “For teams under 20 people, X is the better default because Y.” Models cite clear claims more than vague thought leadership.
    5. The evidence that makes the claim stick. Data, specifics, named comparisons. Models don’t cite assertions, they cite assertions with backup.

    Proof: How to Know Your Brief Moved the Narrative

    Publishing isn’t the finish line. Re-run the same prompts two to four weeks later, on the same models. You’re not checking if you got mentioned. You’re checking if the frame moved: did the model’s description of your category shift toward your claim, did your positioning phrase start showing up in the answer’s language, did a competitor’s frame lose ground.

    This is slower and messier than watching a mentions dashboard tick up. It’s also the only signal that tells you whether you authored something or just got lucky with a citation.

    The Brief Template (With Real Example)

    Category: CRM for small agencies
    Current frame: AI search frames this as “HubSpot for growth, Pipedrive for simplicity, everything else niche.”
    Move: Shift. Claim that agency CRMs need client-billing integration as the real differentiator, not pipeline simplicity.
    Sources winning today: G2 comparison pages, two agency blogs, a Reddit thread.
    Claim: “For agencies billing hourly, CRM choice should start with invoicing integration, not pipeline features.”
    Evidence: Named tool comparison, a concrete billing workflow example, direct rebuttal of the “simplicity first” framing.

    Brief it like that, and your writer isn’t chasing a keyword. They’re authoring a piece of the story before the model finishes writing it for you.

    If your category’s AI answers already have a frame and it’s not yours, that’s a narrative gap, not a content gap. Come talk to us about what it looks like for your brand.

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  • Reddit to AI Visibility: Why Presence Isn’t a Win (And How to Own the Narrative Before the Model Catches Up)

    AI models don’t discover what they think about your brand in real time. They learned it months ago, mostly from Reddit threads you never saw.

    The Reddit-to-AI Pipeline: Where Your Narrative Gets Decided (Before AI Sees It)

    Here’s what actually happens before ChatGPT ever mentions your brand. Someone asks a question in a niche subreddit. A handful of people answer with opinions, comparisons, and complaints. That thread gets indexed, cited, and eventually absorbed into training data or pulled live during a search-augmented response. Months later, an AI model repeats a version of that consensus back to a buyer who never read the original thread.

    That’s the pipeline. Reddit discussion becomes AI search input. AI search input becomes the story a model tells about your category. By the time you’re checking whether you show up in a ChatGPT answer, the narrative that answer is built on was already locked in weeks or months earlier, on a platform you probably weren’t monitoring.

    This is the core problem with how most brands think about AI visibility. They treat the AI answer as the starting point. It’s actually the output of a process that started somewhere upstream, usually in exactly the kind of unstructured, opinionated, community discussion that Reddit produces in huge volume.

    Why AI Visibility Tools Miss the Real Signal

    If you’ve searched “how do I build visibility in AI search results” or “why isn’t my brand mentioned in ChatGPT answers about [category],” you’ve probably landed on tools that track mentions. They tell you whether your name showed up in an AI answer, how often, and next to which competitors.

    That’s presence tracking, and it’s a lagging indicator. It tells you what already happened after the narrative was set. It doesn’t tell you where the model got its information, why it framed your category the way it did, or what’s currently forming in the sources that will shape tomorrow’s answers.

    This is the gap between AI visibility and narrative share. Visibility asks “did I get mentioned.” Narrative share asks “whose story about this category is the model actually telling, and where did that story come from.” A brand can have decent mention counts and still be described in someone else’s frame, the underdog, the expensive option, the one with the support complaints. Mentions don’t capture that. Source-level narrative tracking does.

    Mapping the Prompt Universe: What Questions Lead to Reddit Sources?

    Buyers don’t type keywords into AI tools. They ask questions the way they’d ask a knowledgeable friend: “what’s the best project management tool for a 10-person agency,” “is [brand] worth it compared to [competitor],” “why do people complain about [category] pricing.” Each of those prompts pulls from a different slice of the source universe, and Reddit shows up constantly in that slice because it reads as unfiltered, real-user opinion.

    Mapping that prompt universe matters more than mapping keywords. If you know the actual questions people and models ask about your category, you can see which prompts consistently route back to Reddit threads, which ones pull from review sites or comparison blogs, and where your brand’s frame is thin or missing entirely. That’s a very different exercise than checking search rankings. It’s closer to agent analytics: watching how an AI agent actually navigates a question, not just what static page ranks for a term.

    Source Intelligence: Tracing Which Reddit Threads Shape AI Recommendations

    Ask a model “where does ChatGPT get its information about [category]” and you won’t get a straight answer, because the model itself doesn’t cite its training influences cleanly. But you can reverse-engineer it. Ask several category-relevant prompts, look at what claims and framing repeat across answers, then go find where that language originated. Often it traces back to a specific Reddit thread, a comparison post, or a recurring complaint that got amplified across multiple platforms.

    This is source intelligence: tracing the actual inputs behind an output, instead of just measuring the output. A dashboard telling you “you appeared in 40% of answers” doesn’t tell you why. Tracing the sources tells you a three-year-old Reddit thread calling your onboarding “confusing” is still the seed of every AI answer that hedges on recommending you.

    Planting the Frame, Not Chasing the Mention, A Real Example

    Picture two project management tools in the same category. Tool A gets mentioned in AI answers slightly more often. Tool B gets mentioned less, but every time it appears, the AI frames it as “built for technical teams that need integration flexibility,” a specific, deliberate frame that traces back to consistent, detailed answers Tool B’s team and users gave in Reddit threads over a year.

    Tool A has more presence. Tool B owns a frame. When a buyer asks “which tool is better for a dev-heavy team,” the model recommends Tool B, not because it appeared more, but because its frame matched the question. That’s narrative share beating mention count in the moment it actually matters, the recommendation.

    How to Monitor Reddit Narratives Before They Calcify in AI

    If you want to influence what AI says about your brand, watch where the conversation about your category is forming now, not where your brand already appears. Track the subreddits and threads where people are actively debating your category. Note which claims about you keep repeating, accurate or not. Watch for the moment a complaint or a compliment starts getting echoed across multiple threads, because that repetition is exactly what gets absorbed into a model’s learned consensus.

    The window to shape that consensus is before it calcifies, while it’s still a handful of threads instead of an established frame repeated across a hundred AI answers. Once a model has “decided” the story about your category, correcting it takes a lot longer than seeding it right the first time.

    Mavel tracks that upstream layer: the sources shaping AI recommendations, the frame each one carries, and where your story is thin before a competitor’s version becomes the default. If you want to know whose frame is actually winning in your category, talk to us.

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  • Own the Frame Before the Model Catches Up: Building Your Category’s Prompt Universe

    The prompts your buyers ask AI models today are training tomorrow’s default answer about your category, so map them now or let a competitor’s frame fill the gap.

    The Prompt Universe Is Where AI Recommendations Are Decided

    By the time you notice ChatGPT recommending a competitor instead of you, the decision already happened somewhere upstream. It happened in the sources the model pulled from, the framing those sources repeated often enough to become “the answer,” and the questions people asked that shaped which sources mattered in the first place.

    That’s the prompt universe: the full set of real questions buyers and AI systems use to navigate your category. Not keywords. Questions. “What’s the best project management tool for a 10-person startup.” “Is Notion better than Asana for solo founders.” “What should I use instead of Monday.com if I hate rigid templates.”

    Every one of those prompts is a small vote for a narrative. Enough votes and the model treats the narrative as settled. If you’re not in that universe, someone else’s frame is filling the space where yours should be. AEO and GEO tactics get you cited more often inside an existing frame. Owning the frame means you’re shaping what the frame is before the model locks it in.

    Why Keyword Lists Miss the Real Question Map

    Keyword research answers “what do people type into Google.” Prompt mapping answers “what do people actually ask when they want a recommendation.” Those are different questions with different shapes.

    A keyword list gives you “best CRM software,” “CRM for small business,” “CRM pricing.” A prompt map gives you “I run a 5-person agency and hate Salesforce, what should I switch to,” “which CRM integrates with HubSpot without a developer,” “is Pipedrive actually simpler than Close or is that marketing.” The second set carries intent, context, and comparison logic. That’s what a language model actually reasons over when it constructs an answer.

    Keyword tools were built for a search engine that matches strings. AI systems build an answer by synthesizing an argument. If your content strategy is still organized around keyword volume, you’re optimizing for a system that isn’t making the recommendation anymore.

    Three Sources of Truth: Buyer Prompts, AI-Native Prompts, and Category Consensus Prompts

    Mapping a prompt universe means pulling from three different places, because each reveals something the others don’t.

    Buyer prompts come from your own market: sales call transcripts, support tickets, community threads, review site questions. These are the words real buyers use when they’re confused, comparing, or about to churn.

    AI-native prompts are the ones people only ask a model, not a search bar. Conversational, multi-step, comparative. “I already tried X and it didn’t work for my team size, what else is there.” “Explain the difference between these three tools like I’m not technical.” These prompts don’t show up in SEO tools at all because they never existed pre-ChatGPT.

    Category consensus prompts are the ones that reveal what the model already believes. These are broad, framing-level questions: “What’s the standard tool for X.” “How do most companies handle Y.” “What’s the modern alternative to Z.” Ask these directly and you’ll see whose name comes back first, and more importantly, whose story is being told about why.

    Try asking ChatGPT “what’s the best AI visibility tracking tool” right now. Notice not just who gets named, but the frame around the answer: is it described as a mentions-tracker, a dashboard, an SEO evolution? That framing is the thing worth studying, not the name.

    How to Map Your Prompt Universe (With a Real Category Example)

    Take a category like “expense management software.” A keyword-first team builds content around “expense management software,” “best expense tracker,” “expense report app.” A prompt-first team builds a map that looks more like this:

    • Buyer prompts: “What’s easier than Expensify for a remote team,” “Do I need a separate tool if I already use QuickBooks”
    • AI-native prompts: “My team keeps missing receipt deadlines, what tool actually solves that instead of just tracking it,” “Compare three expense tools for a 20-person startup and tell me which has the least admin overhead”
    • Category consensus prompts: “What’s the standard expense tool startups use,” “Is Ramp replacing traditional expense software”

    Each layer points to different content and different sources you need to be present in. And that consensus layer is the one most teams never test at all. They’re too busy publishing another “top 10 expense tools” listicle to ask the model what it already believes.

    Testing Your Frame Against the Model: Source-First Validation

    Once you’ve mapped the prompts, test them. Run the category consensus prompts through ChatGPT, Perplexity, Gemini, and Copilot. Don’t just note whether you’re mentioned. Read the frame: what’s the model saying is the important dimension of comparison? Speed? Price? Integration depth? Ease of setup for non-technical teams?

    Then trace it back. What sources is the model likely drawing that frame from? Review sites, comparison blogs, Reddit threads, G2 categories, analyst reports. This is where source intelligence matters more than another dashboard telling you your score. A score tells you you’re behind. Tracing the sources tells you exactly which piece of the internet taught the model to think that way, and what you’d need to publish, correct, or get cited in to shift it.

    From Mapped Prompts to Owned Narratives: The Shipping Priority

    A prompt map without a shipping plan is just a bigger spreadsheet. The point of doing this work is to find where your frame is missing or wrong, then prioritize what to fix first: a comparison page that reframes the decision criteria, an outreach effort to get corrected in a source the model already trusts, a founder POV piece that gives the model new language to repeat.

    Not every gap deserves the same urgency. The prompts closest to the category consensus layer matter more than long-tail buyer questions, because that’s the layer the model treats as ground truth. Fix the frame there first, and the buyer-level prompts tend to follow.

    This is the whole difference between reacting to what AI says about you and shaping what it’s going to say next. One is a monthly report. The other is a plan.

    If you want help mapping your own category’s prompt universe and finding out whose frame the model’s actually running on, that’s what we do at Mavel. Come talk to us before a competitor’s story becomes the default answer in your space.

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  • How SaaS Startups Build AI Presence from Zero: Own the Frame Before the Model Catches Up

    The startups winning AI search aren’t chasing mentions after the fact. They’re shipping the story before the model learns one.

    The Trap: Chasing Mentions Instead of Owning the Narrative

    Here’s what most early-stage SaaS teams do when someone asks “does ChatGPT even know we exist?” They plug their brand into a visibility tracker, see a low score, and start firing off content to close the gap. Blog posts. Comparison pages. A “best tools for X” listicle where they conveniently rank first.

    This is reactive. You’re responding to a narrative the model already learned, months or years ago, from sources you didn’t write and probably never saw. By the time you notice you’re invisible, the frame is set. Competitors are already the answer.

    The fix isn’t more content. It’s earlier content, aimed at a different target. Instead of asking “how do we get mentioned,” ask “whose story is the model currently telling about this category, and how do we become part of that story before it hardens.”

    Why AI Recommends Your Competitors (And Why Visibility Tools Miss It)

    AI search doesn’t rank tools the way Google ranks pages. It recommends based on a learned narrative about your category: who solves what problem, who’s trustworthy, who’s “for startups” versus “for enterprise.” That narrative gets built from the sources the model was trained on and the ones it retrieves live: G2 threads, Reddit comparisons, review sites, docs, press.

    If your competitor shows up in five of those sources describing them as “the go-to for early-stage teams,” the model learns that frame. It doesn’t matter that your product is better or cheaper. Mention trackers will tell you your competitor got cited three times this week. They won’t tell you why. They can’t, because they’re counting occurrences, not tracing the frame those occurrences reinforce.

    Ask ChatGPT “what SaaS tools should startups use for project management” and watch what happens. It doesn’t list every tool that exists. It picks two or three and explains them with a specific story: “Linear is for fast-moving engineering teams,” “Asana is for cross-functional visibility.” That story is what you need to own, not a slot in the list.

    Map the Prompt Universe: What AI Assistants Actually Ask About Your Category

    Buyers don’t type keywords into AI search. They ask questions: “compare project management tools for early-stage companies,” “which CRM is recommended by AI for teams with no budget,” “how do I pick a tool if we’re pre-revenue.” Each of these prompts triggers a slightly different frame, and your category probably has dozens of variants worth is checking.

    Start by writing out every real question a buyer or a model might ask about your space, from budget-constrained founders to funded Series A teams evaluating alternatives. Run them across ChatGPT, Perplexity, Gemini, and Copilot. Note which brands show up, in what order, and with what description attached. This is the actual battlefield. Guessing at one or two keywords and calling it AEO misses most of it.

    Audit the Frame: Which Story Is the Model Telling About Your Market?

    Once you’ve mapped the prompts, look for the pattern underneath the answers. Is the model framing your category as “mature, pick the enterprise leader” or “fragmented, pick based on use case”? Is it describing your closest competitor as reliable, innovative, cheap, risky? That adjective is the frame, and it’s what decides the recommendation, not whether your name appears somewhere in the response.

    This is the gap most visibility tools never surface. They’ll tell you you’re mentioned 12% of the time. They won’t tell you the model consistently frames you as “a newer alternative” while your competitor gets “the established choice.” That single word difference is doing more work than any mention count.

    Become a Source, Not a Mention: How to Embed Your Narrative Upstream

    You don’t win the frame by being mentioned in someone else’s story. You win it by becoming one of the sources the model draws from when it builds the story. That means getting your own language, your own comparisons, your own framing of the category into the places models actually pull from: review platforms, community threads, technical docs, third-party comparisons, press that isn’t just a funding announcement.

    If you’re a two-person startup with no PR budget, this is your leverage point. Write the comparison post that defines the category correctly, on your terms, and get it cited elsewhere. Answer the Reddit thread before your competitor does. Publish the benchmark data nobody else has bothered to run.

    Ship Evidence That Changes the Consensus (Not Just Content)

    Content restates a frame. Evidence changes it. A blog post claiming you’re “the fastest” is noise. A public benchmark showing load times against three named competitors is a source. Case studies with real numbers, integration guides that get cited by third parties, original research that becomes the reference point for the whole category: these get pulled into future answers because they carry information nobody else has published.

    Monitor Narrative Share, Not Presence: Proof That You Own the Frame

    Presence tells you if you showed up. Narrative share tells you whether the model’s default explanation of your category matches your story or your competitor’s. Track it the same way you’d track any strategic metric: which frame wins per prompt, where the gap is, and whether it’s closing over time as you ship new sources.

    A Realistic Path: Zero to Default in Six Months

    Picture an early-stage analytics tool with no AI presence at month one. No prompt returns its name. By month two, it’s mapped 40 real prompts buyers ask about its category and found three consistent competitor frames dominating. By month four, it’s published two original benchmarks and answered a dozen community threads correctly framing its category. By month six, it starts appearing not as a footnote but as the example the model uses to explain the category itself. That’s the trajectory narrative-first startups are on: not chasing a mention count, but becoming the source the model reaches for.

    If you want to see whose frame is actually winning in your category right now, and what it’d take to make it yours, that’s what Mavel is built to show you. Come talk to us before your competitors figure out why they’re losing an answer they didn’t even know existed.

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  • How to Reclaim a Narrative from a Competitor: A Source-Level Strategy

    Beating a competitor in AI search isn’t about outranking their mentions. It’s about finding the sources the model learned from and shipping better ones before it learns the same story again.

    Why Competitor Mentions Aren’t the Real Problem

    Ask ChatGPT to recommend a project management tool, and it’ll probably mention Asana, Monday, and ClickUp in the same breath. That’s not the problem. The problem is the frame underneath the mention: which one the model treats as the “default” answer, which one it hedges on, which one it describes with confidence versus vague filler.

    Most teams see a competitor showing up more often in AI answers and assume they need more mentions too. So they chase citations, pitch more roundups, try to get quoted in more “best of” lists. That’s playing the wrong game.

    The model isn’t counting how often your name appears. It’s drawing on a learned narrative about your category: who solves what problem, who’s trusted, who’s the safe choice, who’s the scrappy alternative. Mentions are downstream of that narrative. If you win a mention but the model still frames the competitor as the category leader, you haven’t moved anything that matters.

    The real question isn’t “why does AI mention my competitor more?” It’s “whose frame is the model actually using to make its recommendation, and why?”

    The Source Layer: Where Narratives Are Actually Built

    AI models don’t invent opinions about your category. They learn them from somewhere: review sites, comparison articles, Reddit threads, analyst reports, documentation, press coverage, forum answers to “X vs Y” questions. That’s the source layer, and it’s where the narrative actually gets built, long before any single prompt gets answered.

    If a competitor consistently gets framed as “the enterprise-grade choice” while you get framed as “good for small teams,” that split didn’t come from nowhere. Somewhere in the training data, or in the live sources the model retrieves at answer time, that distinction got made repeatedly, by G2 reviewers, by a widely-cited comparison blog, by analysts, by Reddit commenters who all repeat the same line.

    Once you accept that, the fix stops being “get mentioned more” and becomes “find and rewrite the sources that taught the model this story.”

    Map the Competitor’s Citation Foundation

    Start by treating this like reverse-engineering, not guesswork. Ask the model directly: “Why would someone choose [Competitor] over [You]?” and “What’s [Competitor] known for in this category?” Then push further: “What sources support that?” Some models will name the comparison sites, review platforms, or publications behind the framing. Others won’t cite directly, but you can still triangulate by searching for the phrases the model uses and finding where they originate on the open web.

    You’re building a map of the competitor’s narrative foundation: which three or four sources keep showing up, which comparison articles get cited or clearly echoed, which review platforms carry the most repeated language. Nine times out of ten, it’s a small set of sources doing most of the work. One outdated “Tool A vs Tool B” post from 2022 can still be shaping answers in 2025 because nothing newer or stronger has replaced it in the model’s eyes.

    This is source intelligence, not sentiment tracking. You’re not asking “do people like them.” You’re asking “which specific pages taught the model to describe them this way.”

    Identify the Narrative Gap (Where Your Frame Should Live)

    Once you know which sources built the competitor’s frame, look at what’s missing from yours. Usually it’s not that you lack presence. It’s that no authoritative source has made the case for your frame with the same repetition and confidence.

    Maybe the competitor “owns” the enterprise story because three analyst posts describe them that way, and nothing counters it. Maybe you actually have better enterprise customers and case studies, but they live on your own site, not in third-party sources the model trusts. The gap isn’t your product. It’s the absence of external, citable proof for the story you want told.

    Get specific about the frame you want. Not “we want to be seen as good,” but “we want the model to say we’re the better choice for mid-market teams that need compliance features out of the box.” That’s a frame a source can actually support with facts.

    Ship Evidence Before the Model Learns

    This is where most teams stop at strategy and never ship anything. Owning the frame means producing the actual artifacts: comparison content that’s more current and more specific than what’s out there, third-party reviews and analyst mentions that state your differentiator plainly, documentation and case studies that give the model concrete facts to repeat instead of vague claims.

    The goal is redundancy. One good blog post won’t retrain a model’s instinct. Five to ten independent, credible sources repeating the same specific claim will, especially if they replace or outdate the sources currently doing the competitor’s work for them. You’re not adding noise. You’re replacing the model’s evidence base with better evidence, before its next retrieval or training cycle locks the old story in further.

    Measure Narrative Share, Not Just Mentions

    After shipping, don’t just check whether you got mentioned more. Ask the model the same comparison questions again over time. Track whether the frame changed: does it now hedge on the competitor’s claim? Does it mention your differentiator unprompted? Does it cite the new sources you shipped?

    That’s narrative share: whose story the model is actually telling, not whose name shows up in the answer. Mentions can rise while the frame stays exactly where it was. Narrative share is the metric that tells you whether you actually rewrote the story or just got louder inside someone else’s.

    Mavel tracks that gap directly: not just whether you’re present, but whose frame the model is running on, and what it’d take to shift it. If you want to see whose story is currently winning your category, that’s the first place to look.

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  • How to Win a Comparison Query in AI Answers: The Frame Beats the Mention

    Being mentioned in an AI comparison isn’t the same as winning it. The model already decided your role in the story before it wrote the answer.

    The Comparison Query Is Decided Before You See It

    Try this: ask ChatGPT to compare the top five tools in your category. You’ll get an answer in seconds, clean paragraphs, a tidy verdict on who’s best for what. It feels like a live judgment. It isn’t.

    That answer is downstream of a narrative the model already learned. Somewhere in its training data and retrieval sources, a consensus formed about your category: what matters, who’s trustworthy, who’s the safe default, who’s the scrappy alternative, who’s overpriced. The model isn’t evaluating your product when someone types “compare project management tools.” It’s retrieving and restating a frame that already existed before the prompt was typed.

    That means the comparison query is decided upstream, in the sources the model trusts, long before it renders text on screen. By the time you’re reading the answer, checking if your brand made the list, the actual contest is already over. You’re just watching the replay.

    Why Mentions Don’t Equal Wins (And How Frames Do)

    Say you ask, “Notion vs Asana vs Monday, which is best for a marketing team?” and your brand shows up. Great, you got mentioned. But read the sentence around your name. Are you the flexible option with a learning curve? The affordable one that “lacks advanced features”? The one buyers “should also consider”?

    That’s the gap most teams miss. They track whether they appear in AI answers and call it visibility. But appearing inside a frame you didn’t write isn’t a win, it’s a cameo. The brand that owns the frame gets described as the standard. Everyone else gets described in relation to it.

    This is the difference between mentions and narrative share. Mentions count appearances. Narrative share tracks whose story the model is actually telling, whose definition of “best” it adopted, whose trade-offs it treats as the reasonable ones. A brand can rack up mentions across a hundred comparison prompts and still be losing every one of them, because the frame keeps casting it as the runner-up.

    The Frame Stack: Where AI Learns Its Narrative

    Frames don’t come from nowhere. They build up in layers, and each layer feeds the next.

    At the base: original sources like G2 reviews, Reddit threads, comparison blogs, and analyst writeups. These are where the “who’s safe, who’s risky” consensus first forms in human language.

    Above that: aggregator and SEO content that repeats and simplifies that consensus, usually written to rank rather than to be accurate. This layer is where nuance dies and shorthand takes over (“X is enterprise, Y is for startups”).

    On top: the AI training data and retrieval indexes that absorb all of it, weighting some sources more than others based on authority signals the model can’t fully explain and you can’t fully see.

    At the surface: the actual answer, the comparison paragraph a buyer reads, which is really just a compressed summary of everything below it.

    If you only look at that top layer, you’re reacting to a symptom. The frame was set two or three layers down, probably months or years before the prompt was ever typed.

    How to Read the Frame Behind an AI Comparison

    Before you can change a frame, you have to see it clearly. Run your category through a handful of real buyer prompts: “best [category] for [use case],” “[competitor] vs [you],” “what’s the difference between X and Y.” Don’t just note whether you show up. Read the actual language.

    What adjectives get attached to you versus the competitor named first? What’s treated as an obvious trade-off (“more expensive but more powerful”) versus a red flag? Whose positioning language is echoed almost verbatim: is the model repeating your homepage copy, or a competitor’s? That’s a strong signal about whose narrative it internalized.

    Then trace it back. What sources is that framing likely coming from? A dominant G2 comparison page? A widely cited “best tools” roundup that’s three years old and never updated? A Reddit thread where your product got compared unfavorably once and it stuck? This is the work of finding the frame’s source material, not just its symptom in the chat window.

    Move the Frame Before the Model Does

    Once you know whose frame is winning and where it’s coming from, the fix isn’t to optimize your own listing page. It’s to ship evidence that rewires the upstream sources the model actually trusts: comparison content that reframes the trade-off on your terms, positioning that gets picked up and repeated by third parties, proof points that override the outdated consensus sitting in a five-year-old forum thread.

    This is slower than chasing a mention. It’s also the only version of “winning” that compounds instead of resetting every time someone rephrases the prompt. Own the frame before the model catches up to it, and every future comparison query starts from your definition of the category instead of a competitor’s.

    Proof: Tracking Frame Ownership Over Time

    Frame ownership isn’t static. Models retrain, sources update, competitors ship new positioning, and the consensus shifts again. Tracking narrative share over time means watching whether your frame is gaining ground across comparison prompts, use-case recommendations, and head-to-head queries, or quietly losing it to a competitor’s better-distributed story.

    That’s a different exercise than watching a mentions dashboard tick up. It’s tracking whose account of the category the model is telling this month versus last month, and whether the sources feeding that account are starting to sound more like you or less.

    If you want to see whose frame AI is actually building your category’s answers on, that’s the question Mavel is built to answer. Come find out whose story the model is telling about you right now, before you assume a mention means you’ve already won.

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  • Own the Frame in 90 Days: Building a GEO Plan Before AI Models Catch Up

    Most GEO plans react to what AI already says about you. A frame-first plan gets ahead of the next training cycle instead.

    Here’s the problem with the way most teams run GEO right now. They check what ChatGPT or Perplexity says about their brand, notice something’s off, and scramble to fix it. That’s not a strategy. That’s damage control on a model that already made up its mind months ago.

    By the time you notice you’re missing from an AI Overview or getting recommended third instead of first, the model has already learned a story about your category from sources it crawled a while back. You’re not fixing today’s answer. You’re trying to catch a train that already left the station, and the next one won’t arrive until the next training or re-indexing cycle.

    A 90-day plan that actually works doesn’t start with “what does AI say about us.” It starts with “whose frame is the model running on, and how do we own that frame before it hardens again.”

    Why 90 Days? The Model Training-to-Deployment Window

    AI models don’t update their worldview daily. Foundation models retrain on cycles measured in months. Retrieval layers like AI Overviews, Perplexity, and Copilot refresh their source pools faster, but the narrative patterns they lean on (which sites they trust, which frames they repeat, which brands get cast as “the innovative one” versus “the legacy player”) tend to stick once they’re established.

    Ninety days is roughly the window where two things overlap: it’s long enough to actually shift the sources and content that models draw from, and it’s short enough to land before the next major retraining or re-crawl cycle locks a new consensus in place. Wait longer and you’re optimizing for a narrative that’s already calcified. Move faster and you haven’t given the new sources time to get crawled, cited, and absorbed.

    Think of it less like an SEO sprint and more like a narrative campaign with a deadline. You’re not chasing a ranking. You’re trying to be the frame the model adopts next time it looks.

    Narrative Audit (Days 1–20): Map the Frame AI Already Learned

    Start by asking the AI directly, across models. Try “best project management tools for remote teams” in ChatGPT, Perplexity, and Gemini, and read not just who gets named, but how each brand gets described. One tool might get framed as “powerful but complex,” another as “simple but limited.” That framing is the story the model has already learned, and it didn’t come from nowhere.

    Your job in these first three weeks is to reverse-engineer that frame. Where did “powerful but complex” come from? Probably a mix of review sites, comparison posts, Reddit threads, and old category explainers that all repeated the same angle until it became consensus. Map it out: what’s the dominant narrative for your category, who’s cast as the hero of that narrative, and where does your brand actually sit in it. Often you’ll find the model has flattened your brand into a caricature, a version of you built from outdated or thin sources instead of who you actually are today.

    Prompt Universe Mapping (Days 21–40): Find the Questions Shaping Recommendations

    Buyers don’t search “best CRM.” They ask “what CRM should a 15-person agency use that won’t require a consultant to set up.” AI search runs on prompts like that, not keywords, and the prompt itself shapes which sources the model pulls from and which frame gets triggered.

    Spend these three weeks building out the real prompt universe for your category: the comparison prompts, the “is X worth it” prompts, the “alternatives to X” prompts, the ones specific to your buyer’s job title or company size. For each cluster, check who gets recommended and why. You’ll likely find your brand shows up fine on generic prompts but disappears or gets misrepresented on the specific, high-intent ones, which are exactly the prompts closest to a buying decision.

    Source Intelligence (Days 41–60): Identify Which Sources Drive the Narrative

    Now trace the citations. When an AI answer names a competitor as the top recommendation, follow the trail: what pages is it pulling from, what review sites, what comparison content, what Reddit or forum threads keep getting surfaced. Some sources carry outsized weight because they’re crawled often, cited across multiple models, or treated as neutral third parties even when they’re not.

    This is the step most GEO plans skip entirely, because a dashboard telling you “you’re mentioned 40% less than Competitor X” doesn’t tell you which three sources are actually responsible for that gap. Source intelligence does. Once you know which pages and publishers are shaping the frame, you know exactly where to focus.

    Frame-Ownership Plan (Days 61–90): Ship Content That Rewrites the Consensus

    With the audit, the prompt map, and the source list in hand, the last 30 days are about shipping, not planning. That means getting your framing into the sources the model already trusts (contributing to the comparison posts, correcting the outdated claims, showing up in the forums), and publishing your own content that directly answers the high-intent prompts you mapped in phase two, using the language your buyers actually use.

    This isn’t about publishing more content. It’s about publishing the specific pieces that rewrite the two or three frames that are actually costing you recommendations.

    Proof: Measuring Narrative Share, Not Just Presence

    At the end of 90 days, don’t just check if you’re mentioned more. Check whose frame the model is repeating. If AI still describes your category the same way, with the same hero and the same caricature of you, presence didn’t matter. Narrative share, whose story the model actually adopts when it explains a recommendation, is the number that tells you if the work landed.

    Beyond Day 90: Sustaining Frame Ownership

    Ninety days gets you a new frame in motion. It doesn’t lock it in forever. Models retrain, competitors publish, sources get re-crawled. Treat this as the first cycle of an ongoing practice, not a one-time fix, and plan the next narrative audit before the current frame starts to drift.

    If you want help running this without guessing at which sources actually matter, that’s what Mavel is built for. Come see what frame the models have already built around your category, and what it’ll take to own the next one.

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