How Austin Heaton Engineers Co-Citation to Dominate AI Search Results

B2B search as a ranking game is long gone. Buyers now ask ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews to compare vendors, recommend experts, and explain which companies are worth trusting. In that environment, the brand that wins is the one AI systems cite, understand, and include in the answer, with co-citation increasingly shaping which companies are associated with authority and relevance.

That is where Austin Heaton’s work as an AEO consultant becomes relevant – he helps companies get cited, quoted, and trusted by AI search platforms through AEO, AI citations, entity authority, digital PR, content strategy, backlinks, and technical SEO.

Co-citation is the lens for understanding that strategy: the repeated association of a brand with the right categories, competitors, sources, and buyer-intent topics.

Why Co-Citation Matters in AI Search

Traditional SEO asks, “Can this page rank?” AI search asks a broader question: “Is this entity trusted enough to include in the answer?”

That changes the meaning of authority. A B2B company can have a technically strong website and still be weak in AI search if the broader web does not confirm what the company wants to be known for. If a brand wants to be recognized for AI search visibility, it needs more than optimized service pages. It needs repeated, credible associations with the category.

Co-citation creates those associations. When a company is mentioned near terms such as answer engine optimization, AI search visibility, ChatGPT citations, Perplexity visibility, Gemini citations, digital PR, and entity authority, those connections help define its place in the market.

For an AEO consultant like Austin Heaton, this matters because AI systems do not only evaluate isolated pages. They interpret relationships between entities. The stronger and more consistent those relationships become, the easier it is for AI systems to understand when a brand belongs in an answer.

From Rankings to Recognition

The old search model rewarded companies that could rank pages. The AI search model rewards companies that are recognized as trusted entities.

Recognition means the company is clearly associated with a specific category. It means the founder, product, or service is connected to relevant expertise. It means credible sources repeat the same story. It means AI systems have enough context to include the brand when buyers ask category-level questions.

An AEO consultant is not simply optimizing pages for keywords. The role is broader: helping a company become cite-worthy across answer engines, AI summaries, and conversational search platforms.

For B2B companies, the lesson is clear. AI visibility is not just about producing more content. It is about becoming easier to recognize, easier to trust, and easier to cite.

That requires consistency across the company’s website, third-party mentions, founder profiles, comparison pages, guest articles, podcasts, case studies, and structured data. If those signals all reinforce the same category association, the brand becomes more legible to both buyers and AI systems.

Co-Citation Is the Infrastructure Behind AI Visibility

Co-citation works because AI systems synthesize patterns. They look at the broader context around a company, not just a homepage or a single optimized article.

If a B2B company wants to be known for AI search visibility, it should appear in contexts where that topic is discussed. If it wants to be known for FinTech SEO, it should be mentioned near FinTech, compliance, payments, SaaS growth, and search strategy.

This is broader than conventional link building. Backlinks still help, but AI search also creates value from the context of the mention itself. A podcast transcript, expert quote, industry roundup, comparison page, or third-party profile can all strengthen the relationship between a brand and its category.

In other words, the link matters. But the surrounding meaning matters too.

Why B2B Companies Are Exposed

B2B buyers rarely make decisions from one search result. They compare vendors, ask peers, review case studies, examine alternatives, and look for proof before speaking with sales. AI search compresses that process into a single conversational answer.

That creates risk for companies with strong websites but weak external validation. Many B2B brands have invested in blog calendars, landing pages, and technical SEO, but they have not built enough third-party authority. Their own content says they are credible. The broader web does not repeat it often enough.

AI search exposes that gap. A company that is not mentioned beside its competitors is harder to classify. A company that is not cited in relevant third-party sources is harder to trust. A company with inconsistent positioning is harder to recommend.

Co-citation addresses this by spreading the company’s authority across the web. It makes the brand part of the category conversation rather than just another vendor making claims on its own domain.

The Austin Heaton Model: Category, Entity, Authority

The useful way to understand Heaton’s strategy is through three layers: category, entity, and authority.

The first layer is category clarity. A brand needs to know exactly what it wants to be cited for. In Heaton’s case, the category centers on B2B SEO, AEO, AI search visibility, AI citations, and revenue-focused search execution. That clarity helps define what the brand should be associated with.

The second layer is entity authority. AI systems need to understand who the entity is, what it does, who it serves, and why it matters. This requires structured content, consistent language, clear positioning, and technical signals that make the brand easier to classify.

The third layer is external authority. This is where digital PR, expert mentions, backlinks, interviews, comparison pages, and third-party citations become critical. They reinforce the category and give AI systems more context for trust.

Together, those layers explain why co-citation matters. A B2B brand needs to define the category, reinforce the entity, and earn enough external validation for AI systems to understand why it belongs in the answer.

Measuring AEO Requires Different Metrics

If AI search changes visibility, it also changes measurement.

Traditional SEO metrics such as rankings, impressions, backlinks, and organic sessions are still useful, but they do not capture the whole picture. An AI answer may mention a company without generating a click. A buyer may see a brand in ChatGPT, remember it, and search for it later. A competitor may dominate AI recommendations even while traditional rankings look stable.

That is why AEO measurement needs to include citation frequency, AI share of voice, mention accuracy, sentiment, competitor presence, branded search lift, and conversion quality.

This is where an AEO consultant differs from a traditional SEO-only operator. The goal is not merely to move a page from position six to position three. The goal is to understand whether AI systems mention the brand, how they describe it, which competitors appear beside it, and whether those mentions influence pipeline.

For B2B companies, that measurement shift is critical. AI search visibility may not always appear as a clean last-click session, but it can still shape demand, branded search, vendor consideration, and sales conversations.

Why Most B2B Companies Will Get This Wrong

Many B2B companies will respond to AI search by doing more of the same. They will publish more blog posts, add more FAQs, and lightly rewrite content for conversational queries. Some of that will help, but it will not be enough if the brand lacks external authority.

AI search rewards corroboration: A company’s own website can make a claim, but third-party sources make that claim more believable. Expert mentions, digital PR, comparison content, podcast appearances, case studies, and industry citations all help establish the context AI systems need.

This is why the AEO consultant role is becoming more important. The best AEO consultant is not simply optimizing pages for snippets. They are helping a company build the signals that make it cite-worthy: entity clarity, structured content, technical accessibility, authority mentions, and category relevance.

That is the stronger interpretation of Heaton’s approach. It is not SEO with AI language layered on top. It is a broader visibility system that connects content, technical infrastructure, authority, AI citations, and business outcomes.

Final Thoughts: The Future Belongs to Cited Brands

The future of B2B search will not belong only to the companies that publish the most content. It will belong to the companies that AI systems can understand, trust, and cite.

That requires more than keywords. It requires clear positioning, strong entity signals, third-party validation, expert authority, original proof, and repeated association with the right categories.

Austin Heaton connects AEO with AI citations, entity authority, technical SEO, digital PR, content strategy, backlinks, and revenue measurement. Those claims should be attributed carefully, especially when referencing self-reported results, but the strategic direction is clear.

Co-citation helps explain why this matters – B2B companies need more meaningful associations. They need the market to repeatedly connect their brand with the problems they solve, the buyers they serve, and the categories they want to own.

In the old search model, the goal was to rank – in the AI search model, the goal is to be cited, trusted, and recommended.

That is why co-citation, for B2B companies, is now a core AI visibility strategy.