Search has changed.
It’s no longer just 10 blue links and a battle for position one. AI-generated answers are replacing clicks. Search journeys are getting longer, more conversational, and more fragmented across platforms. Buyers are discovering, comparing, and shortlisting brands inside AI tools before they ever land on a website.
Visibility today isn’t ranking-based, it’s inference-based.
AI systems are deciding which brands to include, summarize, recommend, or ignore. The brands that are failing to adapt won’t just drop in rankings - they’ll quietly disappear from consideration entirely.
At StudioHawk, we’ve been saying this clearly to clients:
Traditional SEO was about ranking pages.
AI Search is about becoming the answer.
Whether you're a small business trying to dominate your niche or an enterprise managing brand presence at scale - this shift applies to you.
In this guide, we’re breaking down:
The best way to understand how AI Search actually works is to compare it to traditional SEO.
Your goal? To rank higher than your competitors. Everything with traditional search is page-based, click-based or position-based.
AI search systems work differently. Instead of simply ranking pages, they:
This is called Retrieval-Augmented Generation (RAG).
What’s the key shift?
You are no longer competing just for a rank - you’re competing for inclusion in the answer.
You can rank #1 but not get cited. You can rank #6 and be summarized. You might not rank at all and still appear - if your brand is well understood as an entity.
That’s a fundamental shift.
"It’s Also Important To Realize That Not All AI Platforms Are Not All The Same"
One mistake that we have seen constantly is companies thinking about AI platforms all in the same way, when in fact they are quite different in what they prioritize and what data they typically synthesize information from.
For example, according to AirOps State Of AI Search 2026 Report, Reddit appears in 68% of citations in Perplexity but just 1% in Gemini.
This means that how you optimize for each of these platforms is going to be slightly different.
Optimization Tips:
If AI can’t clearly describe your business, it won’t recommend you.
Optimization Tips:
Google still uses its ranking system as a base layer - but now it summarizes instead of just listing.
Optimization Tip:
AI Mode (Google’s conversation search evolution) introduces:
This means that content must support evolving intent. If someone searches: “best accounting software for small businesses”, then follows up with “What’s the most affordable option?” AI will consider the entire conversation.
Your content must support layered intent.
This is going to be the biggest shift that we see in search. Google has been steadily moving towards a more “personalized” interface based on where someone is located and if they have regularly engaged with a website previously.
But with Google bringing Personal Intelligence into AI Mode, where users can connect their Gmail and other Google products directly with AI Mode so it can hyper personalize results, their outputs are going to become even more tailored.
There is a lot of noise around “AI optimization” and what you need to be doing in order to show up effectively in search.
With lots of prompt hacks, listicles, random ChatGPT testing and vanity screenshots, the tips and tricks that you might see on LinkedIn and other platforms are muddying the really valuable tactics that are moving the needle for brands.
These are the tactics that actually matter.
We could write a completely separate article about entity-first SEO (and we will!) but as a quick overview: AI systems don’t think in keywords first, they think in entities. Google also extracts entities using a Natural Language Processing algorithm - so this is important for your SEO too!
An entity is a clearly defined “thing”. That “thing” could be:
When an AI system, or even Google, is determining whether to include your business in an answer, it will ask things like:
If your brand positioning changes depending on where someone is looking, AI confidence drops.
By paying attention to your entities and what Google (and likely AI) is associating you with is the ticket to improving your visibility in both areas.
The best way to optimize your entities is to start by understanding what you are “relevant” based on your website’s knowledge graph, build out relevant content around other entities that you want to be relevant for, expand this further through topic clusters and ensure your brand positioning aligns with this everywhere.
1. Extracting Entities You Are Relevant ForWikipedia (already a website that is referenced regularly in LLMs), is a great place to extract the entities you might be considered relevant for. Find a Wikipedia page that best describes the business that you are trying to optimize.
Then, use an entity recognition tool like InLinks or similar to extract the information that surrounds your primary entity.
Then leverage this as the foundation for a content strategy, identifying areas that you need to build more prominence for and where you are less concerned about being visible.
2. Build A Content StrategyNow you understand what entities you are relevant for, you can leverage this information to build out a clear and structured content strategy. Depth and breadth of knowledge in your subject area is a way to show subject authority.
3. Avoid Weak Entity SignalsWeak entity signals can lead to AI not confidently summarizing yoru business in one sentence.
Weak entity signals look like:
The goal with entity-based searches is definability, not just discoverability.
Don’t leave your brand’s content and messaging to vague interpretation - be clear, concise and forthright with everything about your brand, service or product.
Traditional SEO often focused on ranking individual pages - and while this is still important (lets not forget that most LLM usage is for informational purposes not commercial or transactional - yet), AI systems evaluate topical confidence.
One blog post does not equal authority and one optimized landing page does not equal depth.
At StudioHawk, we’ve been focused on building out topical authority for our client’s sites for a long time.
This tactic has been called many things over the years - Hub & Spoke, Pillar Pages and many others. The purpose is to create breadth within a niche, build structured topical coverage, develop contextual relationships between topics and provide reinforcement across multiple documents.
"This should not be specific to just your website. All of your marketing channels should also support this."
Example:
Instead of StudioHawk building out just one published article on “AI SEO Tips”, we will build structured coverage:
The list goes on.
Plus, our social media, our in-person talks, our banners, our booths at expos, our paid media messaging, our whitepapers, will all carry similar messaging to support this.
Now, AI sees:
But let’s take a step back - it’s not just AI that sees that, but also our clients and prospects and anyone seeing our brand.
That signals authority. Topical depth = contextual authority. Contextual authority increases the likelihood of inclusion in synthesized answers.
Structured content for LLM bot parsing is critical. Search is shifting away from keyword retrieval and leaning more towards semantic synthesis, which means the way your content is organized and structured can have a direct influence on whether AI systems can understand, retrieve and reference it. If you’re thinking seriously about AI visibility, structured formatting is no longer optional - it is strategic infrastructure.
Traditional search engines crawl pages and rank documents based on their relevancy for a specific query. LLMs operate differently. They break content into chunks, convert chunks into vector embeddings, compare via cosine similarity, retrieve semantically relevant passages and synthesize responses. If your content isn’t structured properly, it becomes harder for LLMs to chunk cleanly, extract entity relationships, preserve meaning during embedding and retrieve accurately in vector search.
If your content can’t be retrieved cleanly, then the LLMs are less likely to cite or include you.
While this word is becoming as used as “it depends” in the SEO world at the moment, it is important to have a fundamental understanding of what chunking is and how it works.
LLMs don’t read pages the way that humans do. Instead they:
If your content jumps topics mid-section, mixes service definitions with brand fluff, has unclear headers and uses vague transitions, then those “chunks” lose clarity.
Instead, by structuring your pages cleaning with clear semantic boundaries, topic-contained sections and self-sufficient paragraphs, you will dramatically improve retrieval probability.
2. Cosine Similarity & Semantic RetrievalAs mentioned above, when a piece of content is indexed for AI retrieval, it is broken down into chunks and converted into numerical representations called embeddings.
An embedding is essentially a long vector - a list of numbers - that represents the semantic meaning of that chunk in multi-dimensional space. Words that are conceptually similar end up closer together in that space, even if they are not exact keyword matches.
When a user submits a query, that query is also converted into an embedding. The system then compares the query vector to stored content vectors using cosine similarity. Cosine similarity measures the angle between two vectors, not their magnitude. If the angle is small, the vectors point in similar directions, meaning the content is semantically close to the query. If the angle is wide, they are semantically distant.
The important implication for SEO is this: exact keyword matching is no longer required for retrieval. Semantic closeness is what matters.
For example, a user might ask: “how can I fix chronic neck pain from sitting at a desk?”.
A well-structured service page that explains “cervical spine dysfunction caused by prolonged seated posture” could be retrieved, even if it does not contain the exact phrasing “fix neck pain from sitting”.
If the embedding captures the semantic relationship between posture, cervical strain, and desk-related tension, cosine similarity will align those meanings.
This is where structured content becomes critical. Embeddings represent the meaning of whatever is inside a chunk. If a chunk mixes multiple services, vague marketing language, and unrelated ideas, its vector representation becomes noisy. Noise reduces semantic precision and lower precision reduces similarity scores. Lower similarity scores reduce retrieval probability.
On the other hand, when a section is tightly focused, clearly defining a single service, problem or concept, then the embedding becomes more concentrated around that idea. When a query touches that same conceptual cluster, the angle between vectors shrinks and retrieval likelihood increases.
This then shifts how we think about optimization. Instead of just optimizing for keyword density or individual phrases, we optimize for semantic coverage. That means clearly defining related concepts, entities, use cases, symptoms, benefits, and terminology in a structured way.
The more comprehensively and coherently you define a topic, the stronger and more distinct the embedding cluster becomes.
Paragraph construction also influences embedding quality. Dense walls of text can dilute meaning and make chunk boundaries less precise. Shorter paragraphs with clear subject-object relationships tend to embed more cleanly.
Explicit definitions are especially powerful.
For example, stating “Neuromuscular reeducation is a physical therapy technique that retrains the brain-to-muscle connection” creates a high-confidence semantic anchor.
Broad marketing language like “We help you move better” does not.
FAQs have been a particularly powerful tool for user experience, SEO and now GEO for a long time. Why? Because they mirror user queries, map directly into search prompts and improve chunk-query similarity.
People Also Ask rich results have been present in Google for a long time now, so continuing to focus on building these out - provided they are always user first - will help with AI inclusion.
This is one of the most hotly debated topics in SEO/GEO - about whether or not LLMs can render and understand structured data or schema mark up.
At StudioHawk, we feel like whether they can or not right now doesn’t matter. This is because schema can help to reinforce clarity and reduce ambiguity, strengthening the relationships between concepts.
There was a period of time when writing for “systems” or “algorithms” was a core part of SEO - this was back in the keyword stuffy era where an exact match keyword would propel you to the top of search results regardless of how the content itself read.
Then Google recalibrated its algorithms to focus specifically on users - user experience, E-E-A-T, natural language processing - all of this was a vital shift to reduce spam and poor quality SEO tactics from continuing to thrive.
Then what happened?
LLMs began to be used publicly and we’ve seen a rise in “AI slop” across a vast array of multimedia and content. There have been a vast array of AI optimization “tactics” along the way including more listicles, an uplift in press releases and more interesting techniques as hacks to show up in LLMs. Many of them worked for a short while - we know, we tested them ourselves.
But as search continues to expand and we enter the New Consideration Era, there has been a strong need to shift towards writing for both users and retrieval systems. Structuring content for clarity, topic isolation and entity reinforcement.
This isn’t new though - passage indexing introduced by Google in the US in February 2021, also aligned a number of these techniques and the better SEO’s out there were already using a lot of the techniques that are required to show up in AI search before LLMs existed at scale.
To summarize - you are now writing for two audiences: humans and retrieval systems. Finding the balance for both is what is going to propel you in both search and AI search.
Despite the noise suggesting that GEO is replacing traditional SEO, the reality is far more integrated. GEO is built on the foundation that SEO created.
Technical health, crawlability, internal linking, structured data, site architecture and authority signals all still matter because AI systems rely on indexed, trusted content to retrieve and synthesize answers. If your website isn’t crawlable, structured properly and recognized as authoritative within traditional search engines, it is significantly less likely to be included in AI-generated responses. In other words, you cannot win in AI search without first being eligible in search.
The overlap also exists in topical authority and trust signals. Both SEO & GEO reward depth, clarity and expertise.
Strong content clusters, consistent terminology, well-defined service pages, and credible external backlinks strengthen both ranking potential and AI inclusion probability. E-E-A-T signals, brand mention and entity clarity support visibility across traditional SERPs and generative summaries. The difference is not whether these fundamentals matter - it’s in how they are applied.
SEO optimizes for position, GEO optimizes for inclusion. But the structural groundwork that enables both is largely the same.
|
SEO Activity |
How It Helps AI Search |
|
Internal links, content depth |
Helps AI group related content for better answers |
|
Schema markup |
Provides more context about who you are and what you do |
|
Lists, tables, TL;DRs, content structure |
Easier for AI to “chunk” and to cite you |
|
Brand mentions, links, PR |
Builds authority and gets you referenced |
|
Technical SEO, page speed, crawlability fixes |
Essential for AI visibility and is the foundation for crawling and being referenced |
|
Semantic content creation |
Matches how LLMs rank relevance |
If AI search has taught us anything, it’s this: You cannot optimize your way into authority, you have to build it.
Technical SEO and structured content are foundational, but AI systems evaluate far more than your website. They assess your brand footprint, industry presence and overall brand consistency across the web. That means succeeding in AI Search requires a broader strategy than traditional SEO ever demanded - bringing SEO into the holistic marketing funnel rather than operating in a silo.
Backlinks are still highly valuable for brands, but there has been a shift to suggest that brand mentions are also becoming just as important.
Ahrefs ran an Analysis of AI Overview Brand Visibility Factors which highlighted that branded web mentions and branded anchors were two of the most highly correlated factors for brands showing up in AI Overviews.
AI systems analyze a range of signals across the web to determine if your brand is recognized, referenced by credible third parties, appear in expert conversations and are cited in reputable publications. This is where digital PR becomes critical as it helps for your brand to be discussed in multiple trusted contexts (such as industry publication features, thought leadership placements, data-driven studies, commentary in niche media and more). Confidence increases inclusion probability and if your entire digital presence lives only on your own website then your authority ceiling is low.
AI systems favor clarity over generalization and if your brand tries to be everything to everyone, then your entity signal weakens.
Take StudioHawk for example - instead of being a full service digital marketing agency, we are a specialist SEO & AI Search agency. This helps us to create stronger semantic alignment, higher topical authority and clearer recommendation pathways.
For other brands, narrowing down into a particular demographic or niche service that you leverage in your marketing (even if you can offer more), will allow you to have a clearer alignment and dominate your category.
This is one of the biggest structural issues we see. Your SEO team does one thing, PR team does another, the paid ads team has completely separate messaging and builds new pages directing traffic away from some of your top performing organic pages, social media says something different and your sales positioning varies again.
AI systems do not see departments, they see signals. If messaging varies across all of your channels then entity clarity weakens. If positioning changes depending on the platform, confidence drops.
To succeed in AI Search, brands need a unified positioning, consistent terminology, shared messaging pillars, clear differentiation used everywhere and close alignment between all teams.
AI models are also strongly influenced by user discussions, reviews, comparisons, industry debates and community commentary - and brands that generate organic discussion tend to surface more often.
Conversation builds signal and silence is weak - encourage customer reviews, testimonials with detail, case studies with real numbers and ongoing community engagement. The more your brand is talked about online (and offline) the bigger your brand presence in your niche will become and the more likely you are to be referenced.
Understanding what chunking and cosine similarity are is useful, but the real question is how exactly do you apply this to content on your website?
When building any page on your website, whether that’s a landing page, service page, blog article or any other type of content, ask yourself the following questions:
If the answer is yes to all of these questions, then you are increasing inclusion probability. If you answered “no”, then you are potentially reducing it and need to consider reworking your content.
AI visibility is not random, it’s probabilistic. The goal of structured content is to increase the probability that your content matches user intent, scores highly in cosine similarity, are confidently retrieved and are included in synthesized answers.
For more information about SEO & AI Search and what the future of search looks like, call us today on (470) 300-1255 or inquire via our contact form!