LLM Visibility Is Not a Shortcut.
It Is a Trust Architecture.
Part of the GTM Strategy Beyond the Template Series
By Catherine Mak, Founder of ZILU Consultancy | Fractional CMO with 20+ years leading marketing across financial services, B2B scaleups and growth businesses
When I first started paying attention to LLM visibility, I assumed it was an extension of SEO. Fix the website copy, add the right keywords, structure the content correctly — and the AI tools would find you.
Then I searched for myself on ChatGPT. I appeared. Sitting directly next to McKinsey.
That was not a win. That was a signal that something was fundamentally misaligned. I was being cited — but in the wrong context, for the wrong buyers, in a conversation that had nothing to do with the problems I actually solve.
That experience is what made me take LLM visibility seriously as a strategic question, not a technical one. And it is why I now believe it belongs at the very front of go-to-market planning, not as an afterthought, not as a channel tactic, but as the foundational architecture that determines whether everything else you spend on your launch, your content, your PR and your thought leadership actually compounds into commercial visibility.
I recently sat down with Maria Dykstra, an AI Visibility Architect who has spent years working on exactly this problem, to think through what LLM visibility really means for founders and growing businesses. What follows is my perspective on the seven questions that matter most, grounded in that conversation, and in what I have seen building and advising marketing functions at growth stage.
1. What does LLM visibility mean in plain language — and why should founders care?
LLM visibility is not the same as SEO. You can have a perfect SEO score, full technical compliance, optimised metadata across every page, and still not appear in the answers AI tools give to buyers who are researching your category.
The reason is structural. Search engine optimisation is about helping machines rank your content. LLM visibility is about helping machines understand your brand - who you are, what you do, who you serve, and why you are credible enough to be recommended. Those are very different problems.
As Maria explained it:
"AI visibility helps machines make sure they can understand you correctly, define who you are, and help you show up in the right answers at the right time. You're not testing tactics. You're training the machine to describe you correctly to the right buyers." — Maria, AI Visibility Architect
The distinction matters for founders because the cost of getting this wrong is invisible. You will not get a notification that you were excluded from a shortlist. You will not see a drop in traffic. You will simply not be in the conversation at all, and you will have no idea.
Watch Part 1: https://lnkd.in/p/eFQ_uxVZ
2. How are AI tools changing the way buyers discover, compare and shortlist businesses?
The buyer journey was already non-linear before AI changed it further. Buyers were researching independently, moving between channels in unpredictable sequences, and arriving at decisions without following any path a marketer had designed for them.
AI has accelerated and deepened that shift in a way that most marketing measurement infrastructure cannot yet see.
The old model was trackable. Buyers clicked around websites, filled in forms, abandoned carts, came back. All of it fed into analytics. You had a picture of what was happening — imperfect, but directional.
The new model is largely invisible. A buyer goes to ChatGPT and asks a question. They get an answer. They may never visit your website. They may form a clear view of your brand, your credibility and whether they want to talk to you, which is entirely outside any channel you can monitor. And if you are not in that answer, you will never know you were shortlisted out.
"You never know that you lost that client. You never track it, you never retarget it. The shortlist starts forming with one question, and if you're not surfaced in the first three or five recommendations, you're gone." — Maria, AI Visibility Architect
This creates a specific and under-discussed commercial risk: a competitor who has done this work well can be shaping buyer perception of your category before a single person has visited either of your websites. The shortlist is being written in a place neither of you can see.
My own experience reinforces this. A large client came to us through ChatGPT, a brand we had never worked with, in a market we had not actively targeted. They asked where they found us. "ChatGPT," they said. There was no campaign behind that. No paid activity. No referral. Just the accumulated weight of consistent content, clear category positioning and credible presence across multiple sources, compounded into a recommendation by an AI tool we never actively managed.
That is the commercial case for taking this seriously.
Watch Part 2 : https://lnkd.in/p/eUa-_rzc
3. What content and brand architecture does a business need so LLMs can understand it correctly?
This is where most businesses discover that the problem is not what they thought it was.
The instinct is to fix the website. Write clearer copy. Add more keywords. Publish more content. All of that is useful but it misses the more fundamental issue, which is consistency of identity across every surface an AI tool might reference.
LLMs do not read just your website. They look at your LinkedIn, your press releases, your published articles, your podcast appearances, reviews written about you, content written by others in your category, and third-party sources that mention you. They compare all of those signals. Where signals conflict, they become confused. A confused machine does not recommend you. It either produces a wrong description of what you do, or it finds a third-party source — potentially a competitor's website, a listicle, a review platform — and uses that instead.
"If your website has six different definitions of your product and your About page says you're industry agnostic — to a human that means you work with everyone; to a machine it means it cannot classify you. A confused machine doesn't recommend. It goes somewhere else." — Maria, AI Visibility Architect
The architecture that works is simple in principle and harder to execute in practice. One clear category. One consistent description of what you do, written in buyer language — not internal language, not industry jargon, not the language your team uses when you talk to each other about what you build. The language your buyers actually use when they are searching for the problem you solve.
That description needs to be consistent, not identical, because duplicate content creates its own problems, but structurally coherent across your website, your LinkedIn, your authored content, your media appearances, and any third-party platforms where you have a profile.
The question to test this is simple: if an AI tool read everything published about your business from every available source, would it reach one clear, accurate conclusion about who you are and what you do? If the answer is uncertain, that is the architecture problem to solve before anything else.
Watch Part 3: https://lnkd.in/p/eGwu8avv
4. When choosing an LLM visibility tracking tool, what should founders look for — and what should they be careful not to overread?
This is where I have made mistakes personally, and where I see founders make the most expensive errors in this space.
There are now many tools that offer LLM visibility scores. They present a number, sometimes a percentage, sometimes a ranking, that appears to tell you how visible you are. The problem is that those numbers are only as meaningful as the questions the tool asks to generate them. And most tools are asking the wrong questions, or too few of them, or questions that do not reflect how your actual buyers are searching.
My own experience: I was surfaced next to McKinsey on one tool's score. By that tool's metric, that looked like visibility. In practice, it was the opposite. I was appearing in a context that would never produce a relevant buyer for ZILU.
"When you look at the score, don't take it as a verdict. Look into the methodology. What types of questions is the tool asking? Are those questions aligned to your actual buyer questions? A tool might be basing its entire score on one category of query — and if that's not the right category for your business, you could be optimising your content for queries that don't matter." — Maria, AI Visibility Architect
There is also a specific behavioural risk to understand. If a score drops by 10% week on week, the instinct is to immediately rewrite content to chase the previous score. In most cases, that is the wrong response. LLM outputs fluctuate naturally. The models do not produce the same answers consistently across repeated queries. A score change may reflect a real shift , or it may reflect normal model variation that will correct itself without any intervention.
The practical guidance: use tracking tools to establish a baseline and observe directional trends over time, not to make reactive content decisions. Cross-reference any score against your own manual tests and go to ChatGPT and Perplexity, ask the questions your buyers actually ask, and see what comes back. That qualitative test is more useful than any score.
5. How should businesses measure the value of LLM visibility ? How does it connect to pipeline and conversion?
This is the question that determines whether founders take LLM visibility seriously or file it under "nice to have."
The honest answer is that measurement in this space is still developing. LLM traffic does not yet route cleanly through standard analytics infrastructure. A significant proportion of visits driven by AI tool referrals lands in the direct traffic bucket, which means it is effectively invisible in most dashboards. Google Analytics can be configured to capture some LLM-sourced traffic, but even that is incomplete.
What is clear from the businesses that have done this work properly is that the traffic quality from LLM sources is qualitatively different, and commercially more valuable than most other inbound channels.
"The traffic from LLMs is still small, maybe 10 customers compared to 1,000 from Google or direct traffic. But those 10 have already researched you. They're practically committed buyers who are coming to complete the deal rather than doing their research. That's the commercial value." — Maria, AI Visibility Architect
This maps directly to what I have observed in my own business and with clients. Buyers who arrive via AI-tool research have already done the shortlisting. They know more about the business than a paid search click would indicate. They are further along in their decision, and they convert at a higher rate.
The measurement approach that works: configure Google Analytics to capture LLM-sourced traffic as a distinct channel. Build the habit of asking new clients where they first encountered the business, specifically whether they researched through ChatGPT, Perplexity or another AI tool. Track that over time alongside pipeline quality, conversion rate and average deal value. The correlation between LLM-driven discovery and high-value conversion is the commercial case and it compounds as the visibility architecture matures.
6. When should a start-up begin working on LLM visibility? Is it before launch, during, or after building content and PR coverage?
Before. The answer is BEFORE. And I want to be direct about why, because I have seen what happens when this work is skipped at launch and attempted afterwards.
When you launch with fragmented messaging, inconsistent category language, and no clear signal architecture, every piece of content you subsequently publish reinforces the fragmentation. Every press release, every LinkedIn post, every podcast appearance adds another data point that points in a slightly different direction. The AI tools accumulate those signals and form a conclusion about who you are - one that may be wrong, and one that becomes progressively harder to correct as the body of evidence grows.
"Before you put anything out in the world, this is a beautiful time to stop and really create that one message. You're not creating awareness with your content anymore, you're creating evidence. And that evidence will stay with you for a long time." — Maria, AI Visibility Architect
For established businesses, this is genuinely difficult. There is a body of content already published, years of it, in some cases, that was written for a different era, using different language, making different claims about category positioning. That legacy evidence has to be worked against. It is part of why newer businesses are increasingly able to compete with much larger incumbents on LLM visibility: they have no legacy to fight.
For founders at the start of their GTM journey, this is the advantage. The clean slate is not a constraint, it is the opportunity to build the architecture correctly from the first piece of content you publish. Every launch investment, the website copy, the press coverage, the LinkedIn content, the paid media, the event presence, can be aligned to a single, coherent signal from day one. That coherence is what LLMs reward.
The practical implication: LLM visibility architecture should be designed before the first piece of public content is published, and definitely before the launch campaign brief is written.
7. Why is "how fast will this work?" the wrong question? So, what is the more realistic way to think about it?
This is the question I hear most often, and the one I am most direct about.
LLM visibility operates on two timelines simultaneously, and understanding both changes how you think about the investment.
The first timeline is fast. When you publish new content, update your website, or appear in a major publication, AI tools can pick that up within days through live retrieval. Your score can move. You can appear in answers you were not in before. That feels like progress, and it is. But it is also the most unstable form of visibility. It fluctuates with source availability, model updates and query variation. It is not the foundation you should be building on.
The second timeline is slow and it is the one that actually matters commercially. This is the memory that LLMs form about your brand over time: the classification of who you are, which category you belong to, who your buyers are, why you are credible. That memory is built from accumulated evidence across many sources, over many months. It changes with model refreshes, not with individual content updates. And it is extraordinarily difficult to correct once it has been written incorrectly.
"If your LLM memory does not reflect your brand correctly, it will not surface you correctly. And that part takes months to correct, especially for brands that launched with multiple different stories, or that have rebranded. You can change it quickly on the surface, but the deeper memory changes with the model refresh." — Maria, AI Visibility Architect
The businesses that get this wrong are the ones that treat LLM visibility as a performance channel, something to optimise for short-term score improvement, to measure on a monthly dashboard, to switch on and off based on immediate results. That approach produces score fluctuations without commercial impact.
The businesses that get it right treat it as infrastructure. They build the architecture once, correctly, before they scale their content and launch investment. They maintain consistency across all sources over time. They measure directionally, not reactively. And they compound the return over quarters and years, not weeks.
Why This Belongs at the Front of GTM, not the Back
The reason I want to close with this point is that it is still the most common mistake I see in go-to-market planning.
LLM visibility gets added to the plan after everything else has been decided. After the messaging framework. After the channel strategy. After the launch campaign brief has been written. It is treated as a technical layer that can be optimised later, something for the content team to address once the business has built more presence.
That sequencing is backwards and expensive. Every piece of content published without a clear LLM architecture is a piece of evidence working against the coherent signal you eventually need to build. Every press release, every podcast, every LinkedIn post, every paid ad that uses slightly different language is contributing to a fragmented identity that AI tools cannot reliably classify.
The investment required to get the architecture right before launch is small compared to the cost of correcting it afterwards. And the compounding effect of getting it right from the start: where every subsequent content investment reinforces the same coherent signal; what turns LLM visibility from a technical consideration into a genuine commercial advantage.
For founders building a go-to-market strategy right now: this is not the part of the plan you defer. It is the part you design first, before the website goes live, before the launch campaign brief is written, before the first press release is published.
Build the architecture. Define the one clear thing you want AI tools to understand about your business. Make that consistent across every surface. Then let everything else compound on top of it.
That is not a shortcut to visibility. It is the infrastructure that makes the rest of the work worth doing.
Catherine Mak is Founder of ZILU Consultancy. She works as a Fractional CMO with growth-stage businesses, scaleups and organisations navigating go-to-market strategy and AI adoption. This article is part of the GTM Strategy Series: Beyond the Template.
Maria Dykstra is an AI Visibility Architect specialising in LLM presence strategy for B2B brands and growth businesses. Quotes are drawn from a recorded interview and used with permission.