Can You Engineer Your Way to a Great Brand?
TL;DR
- Relevance engineering makes your content citable by AI systems. Brand strategy is what makes AI systems choose you over a competitor with equal technical scores.
- Most SEO tools are still operating on lexical models while search has been semantic since 2012, creating a gap most marketing teams don't know they're in.
- The teams winning in AI search are running technical optimization and brand strategy in parallel, not sequencing one before the other.
At Zero Click SF in early April, Mike King walked on stage, opened with "I told you so," and spent 12 minutes proving it. As CEO and founder of iPullRank, he's been working at the intersection of SEO, data engineering, and AI longer than most people knew that intersection existed. The energy was fast, technical, and combative in the best way. Honestly, one of the better talks of the day.
And it got me thinking about a question he didn't ask.
His central claim: SEO is deprecated. Not dead, but yielding diminishing returns for anyone still playing the old game. The data is hard to argue with. Organic search traffic declined just 2.5% overall in 2025, and Google organic results still capture around 90% of all clicks. The foundation is intact. But 60% of searches now end without a click, with AI summaries answering the question before anyone reaches a website. The game on top of that foundation has changed completely.
Mike's framing for the new game: we are moving from keywords to answers. A keyword gets you ranked. An answer gets you cited. And those are entirely different disciplines. Most SEO tools, he argued, haven't caught up, still operating on a lexical model that counts word frequency while Google has been semantic since 2012. The gap between the tools most marketers rely on and how search actually works is, in his framing, at least a decade wide.
What he's building at iPullRank is something he calls relevance engineering: the confluence of AI content strategy, digital PR, UX, and information retrieval. Technical rigor applied to the question of how content performs inside AI systems. Query fan out, cosine similarity scoring, information gain. This is not about keyword density. It's a fundamentally different way of thinking about why content surfaces and why it doesn't. The level of technical sophistication here is real and worth taking seriously.
Relevance engineering gets you eligible. Brand gets you chosen.
When AI answers a question, it isn't just retrieving the most optimized page. It's synthesizing a response from sources it considers credible, authoritative, and consistent over time. That's not a ranking signal. That's a reputation signal. And you can't engineer your way to a reputation. You can only build it.
Those are not the same tasks, and the gap between them is where a lot of technically excellent content goes to die. You can have perfect cosine similarity scores and a content architecture that maps beautifully to query fan out, and still lose the citation to a competitor whose brand the model has been trained to associate with authority in that space.
We call this the eligibility gap: the distance between being technically findable and being the brand an AI system chooses to recommend. Relevance engineering closes the first half. Brand strategy, positioning, and a coherent digital presence close the second. Most teams invest heavily in one and assume the other will follow. It doesn't.
Where EA lives in this equation
This is where Edgar Allan lives. Not in opposition to what Mike is describing, but in the layer above it. Brand strategy, creative, positioning, and the digital presence that makes all of it coherent and recognizable. Our Visibility Engineering and Optimization methodology connects brand clarity directly to the signals AI systems use to evaluate credibility: consistent positioning, clear category authority, and structured content that tells the same story whether a human or a model is reading it.
What Zero Click SF made clear is that neither lane is sufficient on its own anymore. The technical side needs the brand layer to mean something. The brand layer needs the technical side to be findable. We use Profound to measure the gap between the two, tracking where a brand appears in AI-generated answers, how it's being characterized, and where a competitor is getting cited instead.
The next internet is going to be built by teams that can hold both. Mike King is one of the clearest thinkers on the engineering side of that equation. And the convergence he's pushing toward, even if he wouldn't frame it this way, is exactly where the most interesting work is happening
Part of a series covering Zero Click SF. Next up: Dan Morrell, VP of Marketing Technology at LinkedIn
What's the difference between relevance engineering and AEO?
Relevance engineering, as Mike King frames it, focuses on making content technically readable by AI systems: structured data, semantic clarity, information architecture that maps to how models process queries. AEO encompasses that technical layer but adds brand positioning and entity association. The question AEO asks isn't just "can AI find this content?" It's "when AI synthesizes an answer about this category, does this brand belong on the shortlist?" Those require different interventions.
If my content is technically optimized, why isn't it being cited?
Technical optimization makes your content eligible. It doesn't make your brand the chosen answer. AI systems synthesize responses from sources they associate with authority in a given space, and that association is built over time through consistent positioning, clear category signals, and a body of content that tells a coherent story. If your brand is technically sound but still getting passed over, the issue is usually upstream: the brand story isn't clear enough for an AI model to summarize accurately, or the category you're claiming doesn't match what the model has learned to associate with you.
What does brand strategy have to do with AI search visibility?
More than most teams expect. AI systems form a point of view about brands based on everything they can read about them online: how the brand is described by third parties, how consistently it signals its category, how specific and credible its content is. A vague positioning or an inconsistent digital presence produces a vague AI representation, which means you surface in generalist answers rather than high-intent ones. Brand clarity is the foundation that makes every technical optimization work harder.
How does Edgar Allan approach the connection between brand and AEO?
We start upstream. Before we touch content structure or schema, we make sure the brand story is clear enough that an AI system could describe it accurately in a single sentence. That usually surfaces a positioning problem most clients didn't know they had. From there, we connect brand strategy to site architecture, content structure, and the signals Profound tracks to measure how the brand is showing up in AI-generated answers. The goal is a coherent story that holds from the positioning brief all the way through to what ChatGPT says when someone asks which companies in your space are worth considering.
Do I need to choose between SEO, AEO, and brand strategy?
No, and teams that treat them as competing priorities tend to underperform on all three. SEO builds the foundation. AEO optimizes for how AI systems interpret and cite your content. Brand strategy determines whether any of that optimization is working toward a story worth citing. They're sequential in the sense that brand clarity makes everything downstream more effective, but in practice, the teams winning right now are running them in parallel, not waiting for one to be "done" before starting the next.
At Zero Click SF in early April, Mike King walked on stage, opened with "I told you so," and spent 12 minutes proving it. As CEO and founder of iPullRank, he's been working at the intersection of SEO, data engineering, and AI longer than most people knew that intersection existed. The energy was fast, technical, and combative in the best way. Honestly, one of the better talks of the day.
And it got me thinking about a question he didn't ask.
His central claim: SEO is deprecated. Not dead, but yielding diminishing returns for anyone still playing the old game. The data is hard to argue with. Organic search traffic declined just 2.5% overall in 2025, and Google organic results still capture around 90% of all clicks. The foundation is intact. But 60% of searches now end without a click, with AI summaries answering the question before anyone reaches a website. The game on top of that foundation has changed completely.
Mike's framing for the new game: we are moving from keywords to answers. A keyword gets you ranked. An answer gets you cited. And those are entirely different disciplines. Most SEO tools, he argued, haven't caught up, still operating on a lexical model that counts word frequency while Google has been semantic since 2012. The gap between the tools most marketers rely on and how search actually works is, in his framing, at least a decade wide.
What he's building at iPullRank is something he calls relevance engineering: the confluence of AI content strategy, digital PR, UX, and information retrieval. Technical rigor applied to the question of how content performs inside AI systems. Query fan out, cosine similarity scoring, information gain. This is not about keyword density. It's a fundamentally different way of thinking about why content surfaces and why it doesn't. The level of technical sophistication here is real and worth taking seriously.
Relevance engineering gets you eligible. Brand gets you chosen.
When AI answers a question, it isn't just retrieving the most optimized page. It's synthesizing a response from sources it considers credible, authoritative, and consistent over time. That's not a ranking signal. That's a reputation signal. And you can't engineer your way to a reputation. You can only build it.
Those are not the same tasks, and the gap between them is where a lot of technically excellent content goes to die. You can have perfect cosine similarity scores and a content architecture that maps beautifully to query fan out, and still lose the citation to a competitor whose brand the model has been trained to associate with authority in that space.
We call this the eligibility gap: the distance between being technically findable and being the brand an AI system chooses to recommend. Relevance engineering closes the first half. Brand strategy, positioning, and a coherent digital presence close the second. Most teams invest heavily in one and assume the other will follow. It doesn't.
Where EA lives in this equation
This is where Edgar Allan lives. Not in opposition to what Mike is describing, but in the layer above it. Brand strategy, creative, positioning, and the digital presence that makes all of it coherent and recognizable. Our Visibility Engineering and Optimization methodology connects brand clarity directly to the signals AI systems use to evaluate credibility: consistent positioning, clear category authority, and structured content that tells the same story whether a human or a model is reading it.
What Zero Click SF made clear is that neither lane is sufficient on its own anymore. The technical side needs the brand layer to mean something. The brand layer needs the technical side to be findable. We use Profound to measure the gap between the two, tracking where a brand appears in AI-generated answers, how it's being characterized, and where a competitor is getting cited instead.
The next internet is going to be built by teams that can hold both. Mike King is one of the clearest thinkers on the engineering side of that equation. And the convergence he's pushing toward, even if he wouldn't frame it this way, is exactly where the most interesting work is happening
Part of a series covering Zero Click SF. Next up: Dan Morrell, VP of Marketing Technology at LinkedIn
What's the difference between relevance engineering and AEO?
Relevance engineering, as Mike King frames it, focuses on making content technically readable by AI systems: structured data, semantic clarity, information architecture that maps to how models process queries. AEO encompasses that technical layer but adds brand positioning and entity association. The question AEO asks isn't just "can AI find this content?" It's "when AI synthesizes an answer about this category, does this brand belong on the shortlist?" Those require different interventions.
If my content is technically optimized, why isn't it being cited?
Technical optimization makes your content eligible. It doesn't make your brand the chosen answer. AI systems synthesize responses from sources they associate with authority in a given space, and that association is built over time through consistent positioning, clear category signals, and a body of content that tells a coherent story. If your brand is technically sound but still getting passed over, the issue is usually upstream: the brand story isn't clear enough for an AI model to summarize accurately, or the category you're claiming doesn't match what the model has learned to associate with you.
What does brand strategy have to do with AI search visibility?
More than most teams expect. AI systems form a point of view about brands based on everything they can read about them online: how the brand is described by third parties, how consistently it signals its category, how specific and credible its content is. A vague positioning or an inconsistent digital presence produces a vague AI representation, which means you surface in generalist answers rather than high-intent ones. Brand clarity is the foundation that makes every technical optimization work harder.
How does Edgar Allan approach the connection between brand and AEO?
We start upstream. Before we touch content structure or schema, we make sure the brand story is clear enough that an AI system could describe it accurately in a single sentence. That usually surfaces a positioning problem most clients didn't know they had. From there, we connect brand strategy to site architecture, content structure, and the signals Profound tracks to measure how the brand is showing up in AI-generated answers. The goal is a coherent story that holds from the positioning brief all the way through to what ChatGPT says when someone asks which companies in your space are worth considering.
Do I need to choose between SEO, AEO, and brand strategy?
No, and teams that treat them as competing priorities tend to underperform on all three. SEO builds the foundation. AEO optimizes for how AI systems interpret and cite your content. Brand strategy determines whether any of that optimization is working toward a story worth citing. They're sequential in the sense that brand clarity makes everything downstream more effective, but in practice, the teams winning right now are running them in parallel, not waiting for one to be "done" before starting the next.