{"id":4531,"date":"2026-04-10T14:11:05","date_gmt":"2026-04-10T14:11:05","guid":{"rendered":"https:\/\/solutionsreview.com\/marketing-automation\/?p=4531"},"modified":"2026-04-10T14:21:41","modified_gmt":"2026-04-10T14:21:41","slug":"how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/","title":{"rendered":"How to Get Your Brand Cited by AI: A Primer to GEO & AEO"},"content":{"rendered":"

\"\"<\/a><\/p>\n

Solutions Review’s Tim King offers this primer on how to get your brand cited by AI for optimizing GEO and AEO visibility.<\/strong><\/em><\/p>\n

Generative Engine Optimization (GEO) is the practice of structuring content, authority signals, and distribution strategies so that AI systems like ChatGPT<\/span><\/span>, Microsoft Copilot<\/span><\/span>, and Google Gemini<\/span><\/span> cite your brand within generated answers. Closely related, Answer Engine Optimization (AEO) focuses on ensuring your content is selected, summarized, and surfaced when those systems respond to user queries. Together, GEO and AEO represent a fundamental shift in how visibility is earned in modern search environments.<\/p>\n

For the past two decades, digital strategy has been anchored in rankings. The objective was to appear as high as possible on a list of links, capture the click, and convert the visitor. That model is now being disrupted at the interface level. AI systems no longer return lists. They generate answers. In doing so, they select a small set of sources to inform those answers, often without requiring the user to leave the interface. Visibility, in this context, is no longer determined by position on a results page. It is determined by inclusion in the answer itself.<\/p>\n

This distinction has significant implications for software vendors. In traditional search, success was measured by traffic. In AI-driven environments, success is increasingly measured by citation. If your content is not being referenced by AI systems, it is not participating in the earliest and often most influential stage of the buyer journey. If it is, your brand is shaping decisions before a formal evaluation process has even begun.<\/p>\n

The data behind this shift is beginning to surface in meaningful ways. According to Bing Webmaster AI performance metrics, Solutions Review has been cited more than 630,000 times in Microsoft Copilot over a 90-day period, which equates to roughly 10,000 citations per business day within a single AI system. When extrapolated across the broader ecosystem, including platforms like ChatGPT and Gemini, the total footprint expands to approximately 80,000 citations per business day. This suggests a model of influence that operates continuously, with content being referenced at a cadence that would have been difficult to measure in prior generations of search.<\/p>\n

What makes this particularly important is where these interactions occur. Unlike traditional web traffic, which is often driven by exploratory browsing, AI citations are embedded directly into professional workflows. Users are drafting requirements, evaluating vendors, and conducting research inside tools like Microsoft Copilot, and the information they receive is shaped in real time by the sources those systems trust. In practical terms, this means your content is no longer simply a destination. It becomes part of the decision-making infrastructure.<\/p>\n

This is why GEO and AEO should not be viewed as incremental extensions of SEO. They represent a shift from traffic acquisition to influence architecture. The objective is no longer to attract users to your content. It is to ensure your content is selected and trusted when AI systems generate answers on your category.<\/p>\n

Understanding this shift begins with understanding how AI search actually works, and why most content fails to meet the threshold required for citation.<\/p>\n

How AI Search Actually Works (And Why Most Content Fails)<\/strong><\/span><\/h2>\n

AI-powered search systems do not retrieve and rank pages in the traditional sense. They synthesize responses by selecting, interpreting, and combining information from a limited set of sources they determine to be credible and relevant. The output is not a list of options. It is a constructed answer, and that answer reflects a series of decisions about which sources are trusted enough to inform it.<\/p>\n

At a high level, systems like ChatGPT<\/span><\/span>, Microsoft Copilot<\/span><\/span>, and Google Gemini<\/span><\/span> operate through a layered process that prioritizes authority, structure, and clarity over traditional ranking signals alone. While the exact mechanisms vary, the practical outcome is consistent. A small subset of sources is selected, and those sources disproportionately shape the response that the user receives.<\/p>\n

This creates a very different competitive environment.<\/p>\n

In traditional search, a page could perform well by targeting a specific keyword, optimizing on-page elements, and earning enough backlinks to rank. Even if it was one of many results, it still had an opportunity to capture a click. In AI search, that middle ground largely disappears. Content is either selected and cited, or it is effectively ignored.<\/p>\n

The reason most content fails in this environment is not because it lacks information. It is because it lacks signal clarity<\/em>.<\/p>\n

AI systems are not simply looking for relevant content. They are looking for content that can be confidently interpreted, extracted, and reused within an answer. That introduces a new set of implicit requirements.<\/p>\n

Content that gets cited tends to exhibit several consistent characteristics. It defines key concepts clearly and early, making it easy for AI systems to understand what the page is about. It is structured in a way that allows sections to be isolated and reused without losing meaning. It demonstrates authority through consistent topical coverage, not just isolated articles. And it often includes original data or perspective that differentiates it from the broader pool of similar content.<\/p>\n

By contrast, most content is written for humans navigating a page, not for systems attempting to extract and synthesize information. It buries definitions, relies on vague language, or spreads key ideas across loosely connected sections. From an AI perspective, this creates ambiguity. And ambiguity reduces the likelihood of selection.<\/p>\n

There is also a compounding effect tied to authority. Once a domain is repeatedly selected as a source, it becomes more likely to be selected again. Over time, this creates a feedback loop where a relatively small number of publishers become dominant reference points within AI-generated answers. This is one of the clearest signals that GEO is not simply about optimizing individual pages. It is about building a sustained, category-level presence that AI systems recognize as reliable.<\/p>\n

The implication for software vendors is straightforward. Competing in AI search is not about producing more content. It is about producing content that meets the threshold for inclusion. That requires a shift in how content is defined, structured, and distributed.<\/p>\n

The next step is understanding what that looks like in practice, and how to build a repeatable framework for earning citations at scale.<\/p>\n

The GEO Authority Framework: How Software Vendors Earn AI Citations<\/strong><\/span><\/h3>\n

If AI systems are selecting a narrow set of sources to construct answers, the natural question becomes what separates content that is consistently cited from content that is consistently ignored.<\/p>\n

The answer is not a single tactic. It is the presence of a coordinated set of signals that, taken together, indicate to AI systems that a source is authoritative, reliable, and structurally usable within generated responses.<\/p>\n

To make this actionable, it is helpful to frame GEO as a system rather than a checklist.<\/p>\n

The GEO Authority Framework is built on five core pillars that determine whether a software vendor\u2019s content is selected and cited by AI systems.<\/p>\n

Topical Authority Density<\/strong><\/span><\/h4>\n

AI systems do not evaluate authority at the page level alone. They assess it across a body of work.<\/p>\n

A single strong article is rarely enough to establish credibility. What matters is the density of coverage across a defined category. When a vendor consistently publishes high-quality content on tightly related topics, it creates a reinforcing signal that the domain is a reliable source of knowledge within that space.<\/p>\n

This is why broad, unfocused content strategies tend to underperform in AI search. Authority is not built horizontally across unrelated topics. It is built vertically within a category. The more comprehensively a vendor covers its domain, the more likely AI systems are to recognize and reuse that content.<\/p>\n

Citation-Ready Content Design<\/strong><\/span><\/h4>\n

Content must be written not only to be read, but to be extracted.<\/p>\n

AI systems favor content that can be easily interpreted and repurposed within an answer. This means definitions are introduced early and clearly. Key concepts are explained in self-contained sections. Language is precise and declarative rather than vague or overly stylistic.<\/p>\n

In practice, this often looks like structuring content so that individual sections can stand on their own without requiring full-page context. When a model can lift a concept, summarize it, and integrate it into a response with minimal ambiguity, the likelihood of citation increases significantly.<\/p>\n

Distributed Authority Signals<\/strong><\/span><\/h4>\n

Authority is not built on your website alone.<\/p>\n

AI systems incorporate a wide range of signals when determining which sources to trust. Mentions across third-party publications, consistent brand presence in relevant conversations, and alignment with recognized entities all contribute to a broader perception of credibility.<\/p>\n

For software vendors, this means that content strategy cannot be isolated from broader visibility efforts. Editorial coverage, partnerships, expert contributions, and ecosystem presence all play a role in reinforcing the signals that AI systems use to validate a source.<\/p>\n

Category-Level Ownership<\/strong><\/span><\/h4>\n

AI systems are not selecting sources based on individual keywords. They are selecting sources based on perceived ownership of a topic.<\/p>\n

Vendors that attempt to compete on isolated terms often struggle to gain traction. By contrast, those that build a sustained presence across an entire category are more likely to be recognized as default references.<\/p>\n

Category-level ownership is achieved when a brand becomes consistently associated with a topic across multiple contexts. At that point, inclusion in AI-generated answers becomes less about individual optimization and more about established relevance.<\/p>\n

AI-Aligned Content Structuring<\/strong><\/span><\/h4>\n

Structure is one of the most underappreciated factors in GEO.<\/p>\n

AI systems rely on clear hierarchies, logical flow, and well-defined sections to interpret content. Pages that are difficult to parse or that mix multiple ideas without clear boundaries introduce friction that reduces the likelihood of selection.<\/p>\n

Effective structuring does not require rigid formatting, but it does require intentional organization. Headings should reflect distinct concepts. Paragraphs should develop a single idea at a time. Transitions should reinforce relationships between sections.<\/p>\n

When structure aligns with how AI systems process information, content becomes easier to extract, summarize, and cite.<\/p>\n

–<\/p>\n

Taken together, these five pillars form a practical model for understanding how AI systems evaluate content.<\/p>\n

More importantly, they provide a repeatable framework for software vendors to move from publishing content to building authority that is recognized and reused within AI-generated answers.<\/p>\n

The next step is to examine the specific characteristics that make content \u201ccitable\u201d in practice, and how to operationalize these principles at the page level.<\/p>\n

What Makes Content \u201cCitable\u201d by AI Systems<\/strong><\/span><\/h3>\n

If the objective of GEO is to earn inclusion within AI-generated answers, then the most important question becomes what characteristics make content usable in that context.<\/p>\n

AI systems are not evaluating content the way a human reader does. They are evaluating whether a piece of content can be confidently interpreted, segmented, and integrated into a response without introducing ambiguity or risk. This creates a distinct set of attributes that consistently appear in content that is cited.<\/p>\n

The first and most important characteristic is definition clarity. Content that gets cited almost always defines key concepts early and in explicit terms. This reduces uncertainty for the model and increases the likelihood that the definition itself is reused within an answer. Pages that delay or obscure definitions force the model to infer meaning, which lowers confidence and reduces selection probability.<\/p>\n

The second characteristic is structural independence. AI systems often extract individual sections rather than entire pages. As a result, content performs best when each section can stand on its own. A well-structured article allows a model to isolate a concept, summarize it, and reuse it without requiring additional context from the rest of the page. This is why tightly scoped sections with clear headings tend to outperform long, blended narratives.<\/p>\n

The third characteristic is declarative language. Content that relies on precise, direct statements is significantly easier for AI systems to process than content that leans on stylistic or ambiguous phrasing. Statements that clearly assert what something is, how it works, and why it matters are more likely to be selected than content that implies or suggests. In practice, this means favoring clarity over cleverness in key explanatory sections.<\/p>\n

The fourth characteristic is original insight or data. AI systems are trained on vast amounts of information, which means generic explanations are widely available. What stands out is content that introduces something new, whether that is proprietary data, a named framework, or a distinct interpretation of a topic. The citation data from Solutions Review is a strong example of this. It provides a concrete, differentiated signal that is not easily replicated, making it more likely to be referenced.<\/p>\n

The fifth characteristic is topical reinforcement. Content does not exist in isolation. Pages that are part of a broader ecosystem of related content are more likely to be trusted. When multiple articles from the same domain consistently address related aspects of a topic, it strengthens the overall authority signal. AI systems interpret this consistency as evidence that the source has depth of expertise rather than a one-off perspective.<\/p>\n

The sixth characteristic is entity alignment. AI systems increasingly map content to known entities such as platforms, companies, and technologies. Referencing systems like ChatGPT<\/span><\/span>, Microsoft Copilot<\/span><\/span>, and Google Gemini<\/span><\/span> in a clear and contextual way helps anchor content within recognized ecosystems. This improves the model\u2019s ability to understand where the content fits and increases the likelihood of reuse.<\/p>\n

Finally, there is consistency of terminology. AI systems rely on pattern recognition. When key terms such as Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are used consistently and reinforced throughout a piece, it strengthens the association between the concept and the source. Inconsistent language introduces noise and weakens that connection.<\/p>\n

Taken together, these characteristics form a practical standard for what \u201ccitation-ready\u201d content looks like. They also highlight a broader point. Optimizing for AI is not about gaming a system. It is about reducing ambiguity and increasing confidence at every level of the content.<\/p>\n

For software vendors, this is where execution begins. The framework defines the strategy, but these characteristics determine how that strategy is realized on the page. The next step is understanding how to measure whether these efforts are working, and how to track visibility within AI-generated answers as a core performance metric.<\/p>\n

Measuring GEO Performance: From Traffic to Citation Visibility<\/span><\/h3>\n

If GEO and AEO represent a shift from rankings to citations, then measurement must evolve accordingly.<\/p>\n

Traditional metrics such as pageviews, sessions, and click-through rates were designed for a web where users navigated across links. In AI-driven environments, those signals only capture a fraction of actual influence. A growing portion of content consumption now occurs \u062f\u0627\u062e\u0644 generated answers, often without a click ever taking place.<\/p>\n

This creates a visibility layer that is both highly impactful and largely invisible to conventional analytics.<\/p>\n

Tools like Bing Webmaster Tools<\/span><\/span> are beginning to expose this layer by providing insight into how content performs \u062f\u0627\u062e\u0644 AI-generated results, particularly within systems like Microsoft Copilot<\/span><\/span>. These insights mark an early but important shift toward understanding performance in terms of citation frequency and contextual influence, rather than traffic alone.<\/p>\n

At a practical level, measuring GEO performance involves tracking three emerging dimensions.<\/p>\n

The first is citation frequency. This reflects how often your content is referenced within AI-generated answers over a given period. High citation frequency indicates that your content is being repeatedly selected as a trusted input, which is one of the clearest signals of authority in AI search.<\/p>\n

The second is page-level citation visibility. Not all content performs equally. Some pages become primary sources for AI systems, while others are rarely referenced. Understanding which specific assets are being cited allows teams to identify patterns in structure, topic selection, and framing that contribute to success.<\/p>\n

The third is influence within decision contexts. This is more qualitative but equally important. When content is surfaced inside workflows such as vendor evaluation, requirements gathering, or solution comparison, it plays a role in shaping decisions at a critical stage. In this sense, a single citation in the right context may carry more weight than thousands of passive pageviews.<\/p>\n

This is where the distinction between traffic and impact becomes most clear.<\/p>\n

A traditional content strategy might prioritize maximizing visits to a buyer\u2019s guide. A GEO-aligned strategy prioritizes ensuring that guide is repeatedly cited when AI systems are asked how to evaluate vendors in that category. The former measures attention. The latter measures influence.<\/p>\n

Over time, this shift will likely produce new benchmarks and reporting standards. For now, early adopters have an advantage. Organizations that begin tracking citation visibility today are better positioned to understand how their content is interpreted by AI systems and how that interpretation evolves.<\/p>\n

For software vendors, this represents a meaningful opportunity. While most of the market is still optimizing for rankings, a smaller group is beginning to optimize for inclusion within answers. The gap between those approaches is where competitive advantage is being created.<\/p>\n

The next step is to place GEO within the broader evolution of search, and to understand how it relates to SEO and AEO as part of a larger shift in how information is discovered and consumed.<\/p>\n

From SEO to AEO to GEO: The Evolution of Search<\/strong><\/span><\/h3>\n

Search has not been replaced. It has been restructured.<\/p>\n

What began as a system for retrieving documents has evolved into a system for generating answers. Each stage of that evolution reflects a shift in how information is organized, accessed, and ultimately consumed.<\/p>\n

Search Engine Optimization (SEO) was built around ranking pages. The objective was to position content as high as possible within a list of results, capture user attention, and drive traffic. Success was determined by visibility within that list and the ability to convert a click into engagement.<\/p>\n

Answer Engine Optimization (AEO) emerged as search interfaces began to prioritize direct responses. Featured snippets, knowledge panels, and voice assistants introduced a model where the answer itself became the primary unit of value. Content was no longer competing solely for position. It was competing to be selected as the answer.<\/p>\n

Generative Engine Optimization (GEO) extends this model into a fully synthesized environment. Systems like ChatGPT<\/span><\/span>, Microsoft Copilot<\/span><\/span>, and Google Gemini<\/span><\/span> do not simply retrieve or highlight content. They construct responses by integrating information from multiple sources. In this context, visibility is defined by whether a source is included in that construction process.<\/p>\n

This progression can be understood as a shift across three distinct paradigms.<\/p>\n

    \n
  • SEO prioritized position.<\/em><\/li>\n
  • AEO prioritized selection.<\/em><\/li>\n
  • GEO prioritizes integration.<\/em><\/li>\n<\/ul>\n

    Each step reduces the distance between the user\u2019s question and the final answer. As that distance shrinks, the number of opportunities for a brand to be seen also decreases. In a traditional search results page, there may be ten or more viable options. In an AI-generated response, there may be only a handful of sources informing the output.<\/p>\n

    This compression is what makes GEO strategically significant.<\/p>\n

    It introduces a higher threshold for visibility while simultaneously increasing the impact of being included. When a source is selected within an AI-generated answer, it is not presented as one option among many. It is embedded within the answer itself, often without explicit comparison. This creates a form of influence that is both more concentrated and more subtle than traditional search exposure.<\/p>\n

    It also reinforces the importance of authority.<\/p>\n

    In SEO, authority functioned as a ranking signal among many. In GEO, authority operates more like a filtering mechanism. AI systems rely on trusted sources to reduce uncertainty when generating responses. As a result, the set of sources that consistently meet that threshold becomes disproportionately influential over time.<\/p>\n

    For software vendors, this evolution reframes the objective of content strategy.<\/p>\n

    The goal is no longer limited to driving traffic or even capturing the answer in isolated cases. It is to become a consistent input into the systems that generate answers across an entire category. That requires a shift from optimizing individual pages to building a sustained, recognizable presence that AI systems associate with expertise.<\/p>\n

    Understanding this progression is essential because it informs how resources should be allocated. Investments that were once sufficient for SEO may not translate directly into GEO outcomes. The strategies that succeed are those that align with how AI systems interpret authority, structure, and relevance at scale.<\/p>\n

    This is why the conversation is moving beyond optimization tactics and toward ownership of the narrative itself. The next step is to examine why authority has effectively become the new distribution channel in AI search, and what that means for organizations trying to establish and maintain visibility.<\/p>\n

    Why Authority Is the New Distribution Channel<\/strong><\/span><\/h3>\n

    In traditional digital strategy, distribution was a function of channels. Brands invested in search rankings, paid media, email lists, and social platforms to ensure their content reached the right audience. Visibility was achieved by placing content in front of users.<\/p>\n

    In AI-driven environments, that model is being inverted.<\/p>\n

    Content is no longer distributed outward. It is pulled inward by systems that determine what information is relevant, credible, and useful in the moment a question is asked. The gatekeeping function has shifted from platforms that display options to systems that construct answers.<\/p>\n

    In this context, authority is not simply a ranking factor. It is the mechanism that determines whether content is included at all.<\/p>\n

    Systems like ChatGPT<\/span><\/span> and Microsoft Copilot<\/span><\/span> are designed to minimize uncertainty in their outputs. To do that, they rely on sources that demonstrate consistent expertise, clear structure, and strong external validation. Content that meets these criteria is more likely to be selected, reused, and reinforced over time.<\/p>\n

    This creates a new form of distribution that is not based on reach, but on trust.<\/p>\n

    When a source is repeatedly cited, it becomes part of the underlying knowledge layer that informs future responses. Over time, this leads to a concentration of influence among a relatively small set of authoritative publishers. The practical effect is that authority compounds. The more a source is used, the more likely it is to be used again.<\/p>\n

    For software vendors, this has direct implications for how visibility should be built.<\/p>\n

    Owning distribution channels is no longer sufficient. It is increasingly necessary to become a trusted input into the systems that buyers rely on. That requires more than content production. It requires intentional authority building across a category.<\/p>\n

    This is where the concept of Authority as a Service begins to take shape.<\/p>\n

    Rather than treating content as a series of isolated campaigns, this model focuses on systematically building the signals that AI systems use to determine trust. That includes consistent topical coverage, structured and citation-ready content, and a distributed presence across credible third-party environments.<\/p>\n

    The objective is not simply to publish. It is to become a source that is selected.<\/p>\n

    How Software Vendors Can Get Started with GEO<\/strong><\/span><\/h3>\n

    For organizations looking to adapt to this shift, the starting point is not a complete overhaul. It is a realignment of priorities based on how AI systems evaluate content.<\/p>\n

    The first step is to assess current visibility within AI-generated answers. This includes identifying whether your content is being cited, which pages are being referenced, and in what contexts those citations occur. Tools like Bing Webmaster Tools<\/span><\/span> provide an early view into this layer, particularly within ecosystems like Microsoft Copilot<\/span><\/span>.<\/p>\n

    The second step is to identify gaps in topical authority. This involves evaluating whether your content strategy reflects comprehensive coverage of your category or a fragmented set of topics. Strengthening authority requires building depth, not just volume.<\/p>\n

    The third step is to align content structure with AI interpretation. This includes introducing clear definitions, organizing content into self-contained sections, and using precise language that reduces ambiguity. These adjustments improve the likelihood that content can be extracted and reused within generated answers.<\/p>\n

    The fourth step is to expand authority signals beyond owned media. Mentions, contributions, and partnerships across credible third-party platforms reinforce the perception of trust that AI systems rely on when selecting sources.<\/p>\n

    The final step is to treat GEO as an ongoing system rather than a one-time initiative. Authority is built over time and reinforced through consistency. Organizations that approach this as a sustained effort are more likely to see compounding returns.<\/p>\n

    The Bottom Line<\/strong><\/span><\/h4>\n

    AI-generated answers are quickly becoming the primary interface through which buyers discover and evaluate solutions.<\/p>\n

    In this environment, visibility is no longer defined by where you rank. It is defined by whether your content is selected and trusted as part of the answer itself.<\/p>\n

    This is the shift from SEO to GEO and AEO. It is a shift from traffic acquisition to influence within decision-making moments. And it is a shift that is already underway across the enterprise software landscape.<\/p>\n

    For software vendors, the implication is clear. The question is no longer how to drive more visitors to your site. It is whether your brand is present in the systems that are shaping buyer decisions before those visitors ever arrive.<\/p>\n

    Those that adapt will not simply generate more visibility. They will help define the answers the market receives.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Solutions Review’s Tim King offers this primer on how to get your brand cited by AI for optimizing GEO and AEO visibility. Generative Engine Optimization (GEO) is the practice of structuring content, authority signals, and distribution strategies so that AI systems like ChatGPT, Microsoft Copilot, and Google Gemini cite your brand within generated answers. Closely […]<\/p>\n","protected":false},"author":23,"featured_media":4533,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[5],"tags":[],"acf":[],"yoast_head":"\nHow to Get Your Brand Cited by AI: A Primer to GEO & AEO<\/title>\n<meta name=\"description\" content=\"Solutions Review's Tim King offers this primer on how to get your brand cited by AI for optimizing GEO and AEO visibility.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tim King\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"19 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/\",\"url\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/\",\"name\":\"How to Get Your Brand Cited by AI: A Primer to GEO & AEO\",\"isPartOf\":{\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2026\/04\/Business-Intelligence-3.jpg\",\"datePublished\":\"2026-04-10T14:11:05+00:00\",\"dateModified\":\"2026-04-10T14:21:41+00:00\",\"author\":{\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/#\/schema\/person\/154e152a275103e373e24ada7f2feb5c\"},\"description\":\"Solutions Review's Tim King offers this primer on how to get your brand cited by AI for optimizing GEO and AEO visibility.\",\"breadcrumb\":{\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/#primaryimage\",\"url\":\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2026\/04\/Business-Intelligence-3.jpg\",\"contentUrl\":\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2026\/04\/Business-Intelligence-3.jpg\",\"width\":800,\"height\":400},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/how-to-get-your-brand-cited-by-ai-a-primer-to-geo-aeo\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/solutionsreview.com\/marketing-automation\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"How to Get Your Brand Cited by AI: A Primer to GEO & AEO\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/#website\",\"url\":\"https:\/\/solutionsreview.com\/marketing-automation\/\",\"name\":\"Marketing Automation Platforms | Solutions Review\",\"description\":\"Evaluating Enterprise MarTech, CRM Software, Email Tools & B2B Nurture Platforms.\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/solutionsreview.com\/marketing-automation\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/#\/schema\/person\/154e152a275103e373e24ada7f2feb5c\",\"name\":\"Tim King\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/solutionsreview.com\/marketing-automation\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2026\/02\/oie_zdRblKGU8g95.jpg\",\"contentUrl\":\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2026\/02\/oie_zdRblKGU8g95.jpg\",\"caption\":\"Tim King\"},\"description\":\"Tim is Solutions Review's Executive Editor covering the human impact of AI on the future of work and learning. 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