What is Anthropic?

Anthropic was founded by former OpenAI employees who wanted to build an AI company with a stronger focus on safety. The company is headquartered in the U.S. and is funded, in part, by major investments from technology giants such as Amazon and Google. Its work centers on research into large language models and their responsible use in business and society, supported by its own security and research teams. The company’s name is derived from the Greek word for “human” and reflects its stated mission to develop AI systems for the benefit of humanity.

Note: Anthropic deliberately positions itself as a research company focused on AI safety, rather than primarily as a provider of consumer apps like some of its competitors.

In addition to its own research, Anthropic also offers its models to other companies via a programming interface, so that many products are built on this technology behind the scenes without end users ever seeing the name Anthropic. This API is used by both small startups and established companies to equip their own applications with AI capabilities, for example in customer communication, software development, or internal knowledge management.

• Founded by former OpenAI employees
• U.S. companies focused on AI security
• Funded by Amazon and Google
• Models can also be used by other providers

How is Anthropic connected to Claude?

Claude is the family of language models developed by Anthropic, which are made available as a chatbot and via interfaces for other applications. Similar to how ChatGPT is based on OpenAI models, Claude runs exclusively on Anthropic’s own technology. The models are regularly updated and offered in different tiers for simple or particularly demanding tasks, ranging from quick everyday responses to complex analytical tasks in research and software development.

• Claude is a product of Anthropic
• Available via chat and through interfaces
• Regular releases of new model versions
• Different Levels for Different Tasks

Anthropic Compared to OpenAI and Google

While OpenAI offers ChatGPT, the best-known AI chatbot, and Google integrates its models directly into traditional search and AI search results, Anthropic positions itself more as a technology partner working behind the scenes. For GEO’s own agency strategy, this means that visibility in Claude-based applications is another key component—alongside Google, ChatGPT, and Perplexity—that should not be underestimated.

• OpenAI: The Most Well-Known Consumer Chatbot
• Google: AI Built Right into Search
• Anthropic: A Technology Partner Behind the Scenes
• Relevance for GEO Strategies Is Growing

Why Security Is a Priority at Anthropic

Anthropic often describes its approach as safety-first AI development: New model versions undergo internal testing before being released, and the company regularly publishes research findings on the risks and limitations of large language models. For companies integrating Claude into their own products, this is a key selling point when reliability and traceability are particularly important. This focus clearly distinguishes Anthropic from providers that prioritize rapid growth and maximizing the number of end users, and it also shapes the tone of the company’s own communications.

• Internal security testing prior to release
• Regular research publications on risks
• Focus on Model Traceability
• Relevant for safety-critical integrations

Anthropic for Marketing Teams: Where Claude Pops Up in Everyday Work

Even though Anthropic itself doesn’t offer marketing software, Claude is appearing in more and more tools that marketing teams use every day: as a research assistant for competitive analysis, as a writing tool for initial drafts of campaign copy, or as an analysis tool for large volumes of customer feedback. Those who understand the model’s capabilities and limitations can use these integrations more strategically rather than treating them as a mere black box.

One practical advantage is its comparatively high reliability when handling longer, complex tasks: Claude is often used for tasks that require processing a large amount of context at once, such as analyzing entire campaign reports or multi-page briefings. For teams that work with a large number of documents, this is a practical advantage over tools that can only reliably answer short queries.

• Support research and competitive analysis
• Submit initial draft copy for campaigns
• Analyzing Large Documents and Reports
• Organizing Large Volumes of Customer Feedback

Anthropic and User Visibility in AI Responses

It is becoming increasingly important for companies to appear in Claude’s responses when users ask about products, services, or recommendations. Unlike with traditional SEO, this process cannot be directly controlled; however, experience shows that structured, clearly written content with unambiguous facts is more easily picked up by language models than vaguely worded advertising copy.

• Give preference to clear, fact-based content
• Clear statements instead of vague advertising language
• Structured pages make data entry easier
• Visibility in AI Responses Is Becoming Increasingly Important

Anyone who takes this development seriously will treat Anthropic and similar platforms not as a short-term trend, but as an integral part of their visibility strategy alongside Google and traditional social media channels.

What is Perplexity?

Perplexity is a standalone AI search engine that answers search queries with a summary response in complete sentences, while clearly citing the sources used. Instead of ten blue links, the tool provides a concise answer, supplemented by numbered references to the sources of the information. The name alludes to the technical term “perplexity” from language modeling research, which describes how confidently a model can predict the next word in a sentence. The service is operated by the American company of the same name, Perplexity AI, which deliberately positions itself as an independent alternative to traditional Google Search and offers both a free version and an advanced, paid version.

Tip: If you want to monitor your own brand on Perplexity, you can search specifically for questions that typical customers would ask and check whether and how your website is mentioned.

Behind the scenes, Perplexity combines its own language models with a traditional web search in real time and selects relevant excerpts to support its answer. As a result, the results appear more up-to-date than responses generated solely by language models without internet access, and users can verify each statement directly at the source, which builds trust in the answer.

• An Answer Instead of a List of Links
• Source citations included directly in the answer
• Real-time Web Search in the Background
• An independent company, not a Google product

How does Perplexity cite sources?

Each answer contains small, numbered footnotes that link to the respective source page. Preference is given to pages with a clear structure, unambiguous facts, and up-to-date content, while vague or promotional texts are cited less frequently. Those who want to be cited regularly as a source benefit from the same principles that apply to SEO text optimization: clear structure, verifiable statements, and up-to-date information. Technical markup using structured data can also help make facts more clearly recognizable to such systems.

• Numbered footnotes for each answer
• Clearly structured pages are preferred
• Timeliness has a positive effect
• Advertising copy is rarely cited

Why this is relevant to GEO

Alongside offerings such as Google’s AI search results, Perplexity is one of the channels that should be monitored as part of Generative Engine Optimization. The models behind such tools come from various providers, such as Anthropic or OpenAI, but the principles for achieving good visibility remain similar: clear, fact-based, and well-structured content is cited more frequently than arbitrary promotional copy. Those who consistently apply these principles improve their chances across multiple AI search engines at once.

• An Important Channel Besides Google AI
• Citation Frequency as a New Metric
• Fact-based content is preferred
• Complements traditional search engine marketing

Perplexity vs. ChatGPT

ChatGPT is primarily a chatbot built on a trained language model that can optionally search the web. Perplexity is designed from the ground up as a search tool and places source citations at the center of every response from the very beginning. For businesses, this means that both systems deserve their own attention within a GEO strategy because they weight and cite content differently, and because users may prefer one tool over the other depending on the question.

• ChatGPT: Chatbot with optional web search
• Perplexity: A Search Tool Focused on Sources
• Both cite the content differently
• Monitoring is advisable in both systems

Perplexity Pro vs. the Paid Version: The Differences

In addition to the free basic version, Perplexity offers an advanced, paid version with access to more powerful models and additional features, such as more in-depth research across multiple sources. For companies engaged in competitive intelligence or market research, this access can be worthwhile because the premium version often provides more detailed and better-sourced answers.

If you’re simply tracking your own brand visibility, the free version is usually sufficient, since both versions use similar criteria when selecting sources. The difference lies primarily in the depth and scope of the results, rather than in the basic selection of the cited sites.

For companies with more extensive research needs, Perplexity also offers an advanced plan designed for team use, which allows multiple people to access the same research features collaboratively. However, this advanced plan is generally not necessary for simply monitoring your own brand visibility.

• Free version for initial observation
• Paid version with more in-depth content
• Similar criteria for selecting sources
• It’s especially worthwhile for research

How Companies Check Their Own Visibility on Perplexity

If you want to know whether your brand appears on Perplexity at all, you should enter typical customer questions yourself and carefully review the cited sources. If your website never appears, while competitors are cited regularly, it’s worth taking a look at the structure and recency of their content as a first step.

In addition, an understanding of traditional web analytics helps determine whether visitor numbers from AI search engines are tracked separately at all. A well-maintained, up-to-date sitemap also makes it easier to ensure that new or updated content is indexed promptly.

It’s also a good idea to establish a regular routine—such as a monthly session—during which the same test questions are asked again. This makes it possible to track changes over time, rather than just getting a one-time snapshot that may no longer be accurate just a few weeks later.

• Try Answering Typical Customer Questions Yourself
• Check the sources cited by competitors
• Track AI traffic separately in analytics
• The current sitemap supports indexing

What is AI training data?

AI training data consists of the text, images, and other content used to train an AI model during its development. From billions of words, the model learns patterns, relationships, and linguistic logic without permanently storing the individual sources in plain text. Providers such as OpenAI use publicly accessible websites, digitized books, forum posts, and additionally licensed datasets that are specifically purchased for certain subject areas.

Tip: If you want to check whether your domain appears in common training datasets, you can use specialized checking tools or contact the providers directly.

After the initial training, there is usually a fine-tuning phase involving human feedback, during which real evaluators rate and correct responses to make the model more helpful and reliable. The original training data remains the foundation for the model’s overall language comprehension, while the fine-tuning primarily shapes its tone and confidence.

• Texts from the open web
• Digitized Books and Specialized Literature
• Licensed Partner Data
• Forum and Community Content
• Sample dialogs reviewed by people

Does content from your own website end up in the training data?

Whether a website is actually indexed depends heavily on technical settings and the timing of its publication. Many AI providers’ crawlers respect the robots.txt file and can be specifically blocked or allowed, much like traditional search engine crawlers have been doing for years. Anyone who wants to deliberately make their own content visible—for example, to increase the number of mentions in ChatGPT responses—should therefore not block these crawlers across the board, but rather selectively control which areas of the website should be accessible.

• Robots.txt controls access
• Meta tags can exclude crawlers
• Paywalls prevent content from being read
• Older content has often already been recorded

Why This Is Relevant for Businesses

For marketing professionals, what matters is not so much the technical details, but whether their own brand appears at all in AI responses. Content that was available early on and has remained accessible on the open web has a better chance of having served as a knowledge base for language models and is therefore mentioned more frequently in generated responses. This is a key component of Generative Engine Optimization, which focuses not only on traditional Google rankings but also on visibility in AI-generated responses. Solid SEO text optimization remains the foundation here, as structured, clearly formulated content is easier for both search engines and AI systems to process and cite.

• An Early Online Presence Increases Training Opportunities
• Clear, well-structured texts are preferred
• Check for Brand Mentions in AI Responses
• GEO complements traditional SEO

AI Training Data vs. Real-Time Responses

One key difference is that traditional language models “freeze” their knowledge as of a specific cutoff date: the model is initially unaware of anything that happens on the web after that date. Tools like Perplexity therefore combine a trained model with an ongoing web search to provide up-to-date information that is not yet included in the original training data. For companies, this means that even after training, it remains important to maintain a presence on the open web and keep their information current.

• Training data has a cutoff date
• Real-time search fills in gaps in knowledge
• Both sources are incorporated into the answers
• Timely content remains important over the long term

Copyright and the Debate Over AI Training Data

The use of publicly available content as training data raises legal questions that have not yet been definitively resolved in many countries. Publishers, authors, and individual website operators are increasingly calling for clearer regulations regarding who is permitted to use what content for training commercial models, and under what conditions. For companies, this means at least being aware of their own position on this issue, even if they do not take legal action themselves.

Until a uniform regulation is in place, many website operators have no practical tool other than technical control via robots.txt and meta tags. Those who want to actively promote their own visibility in AI-generated results—such as Google’s AI search results —will therefore have to continue to deliberately allow these bots, while other operators will specifically block them.

Some providers now offer their own mechanisms that allow website operators to explicitly opt out of having their content used for future training, regardless of the general technical block via robots.txt. Anyone familiar with this option can decide for themselves whether their own website should participate in this process or not.

• Legal framework still unclear in many places
• Publishers are calling for clearer terms
• Know your own position on the matter
• Technical control remains a practical tool

Optimize Your Own Content Specifically for Training Data

Anyone who wants to help shape future training data should publish content in a way that makes it easy to understand even without additional context. Clear definitions, a clean structure, and a well-maintained sitemap—as described in the Webmaster Tools Basics —make it easier for crawlers to index content completely and accurately.

Consistency across the entire website is just as important: If information on different subpages contradicts each other, the likelihood that a model will adopt the correct version decreases. Maintaining consistent facts in a central location therefore pays off for both people and future training runs.

Particularly dense, fact-rich pages—such as glossaries or well-maintained FAQ sections—are ideal for this type of optimization because they condense core knowledge into a compact, clearly worded format. Experience shows that such pages are easier to index correctly than long, narrative-style articles with a lot of supplementary information.

• A clear structure makes data entry easier
• Clear definitions are preferred
• Consistency Throughout the Entire Website
• Contradictions Reduce the Chances of a Takeover

What Defines Traditional SEO

Classic SEO encompasses all measures that help a page rank higher in Google’s organic search results. This includes technical fundamentals such as load time and structure, as well as content-related work on keywords, headings, and text. Building backlinks has also always been an integral part of this craft, as has the ongoing maintenance of existing content.

Tip: Without solid technical SEO as a foundation, even the best content will hardly improve your rankings because the bot can’t properly crawl the page.

This discipline has evolved over many years and is based on ranking factors that Google has confirmed either publicly or through independent studies. Those who understand these factors can set priorities rather than randomly tweaking individual settings, thereby saving a lot of time in their daily optimization work—especially when working with a limited budget and scarce resources.

• Technical Foundations: Load Time, Structure, Mobile
• On-page: Keywords, headings, internal links
• Off-Page: Backlinks and Mentions
• Content: Relevance and User Intent

The difference from GEO

GEO, short for Generative Engine Optimization, does not aim for a spot in the search results but rather for a mention within a generated AI response. An SEO/GEO audit therefore examines both levels separately and often uncovers gaps that would remain invisible in a pure ranking analysis.

While traditional SEO relies heavily on keywords and link structure, systems like ChatGPT or Perplexity tend to evaluate a source’s clarity, structure, and trustworthiness rather than just keyword density in the text. As a result, a page can rank well on Google yet still rarely appear in AI responses—a fact that initially surprises many website owners.

• SEO aims to improve search engine rankings
• GEO Focuses on AI Quotes
• SEO uses traditional ranking factors
• GEO evaluates source reliability and clarity

Why Traditional SEO Still Matters

Despite all the talk about GEO, traditional SEO remains the foundation, because generative systems largely rely on content that is already discoverable through good rankings and solid on-page optimization. Those who can be found on Google also have the upper hand with AI search systems. SEO text optimization therefore remains relevant as well, because clear, well-written content serves both target audiences simultaneously: Google and generative systems. Those who treat these two disciplines as separate entities end up sacrificing reach on both fronts, rather than leveraging existing work to its fullest potential.

• Google remains the largest source of traffic
• Good rankings boost AI visibility
• On-page basics apply to both worlds
• SEO and GEO are becoming increasingly complementary

Planning Traditional SEO in Practice

Those who approach classic SEO and GEO together usually start with a technical assessment, followed by an analysis of existing content. Only then is it worth considering how content should be prepared for generative systems, because otherwise it leads to duplicate work and budgets are wasted in two areas at once. In practice, a clear sequence saves the most time and delivers visible results more quickly.

• Check the technical foundation first
• Systematically evaluate existing content
• Optimizing Structure for Humans and AI
• Re-measure results regularly

Traditional SEO: Common Misconceptions About GEO

A common misconception is the assumption that traditional SEO and GEO require completely different technical foundations. In fact, both are built on the same foundation: accessible, fast-loading pages with clearly structured content. The difference lies less in the technology than in the presentation and wording of the content itself.

The importance of traditional backlinks is also often underestimated: They not only affect search rankings but also indirectly strengthen the credibility that generative systems take into account when selecting sources.

• The technical foundation is the same for both disciplines
• The difference lies in the processing
• Backlinks also affect GEO
• Signals of trust work through both systems

Leveraging Existing SEO Content for Both Worlds

If you don’t want to create entirely new content for generative systems, you should start by conducting a content audit. Many pages that already rank well can be further optimized for AI responses with minor adjustments to their structure and wording, without having to start from scratch.

Existing keyword research also provides valuable insights into the questions users are asking—insights that can be directly translated into clearly worded answer sections.

• Check existing content first
• Minor adjustments instead of a complete overhaul
• Use Keyword Research for Questions
• Formulate response sections clearly

Those who view traditional SEO and GEO as two perspectives on the same foundational work—rather than treating them as competing disciplines—can avoid duplicate planning and duplicate budgets. Ultimately, the same foundation applies to both forms of visibility: accessible, clearly structured pages with robust content that both humans and machines can understand immediately.

What is SEO?

SEO encompasses all measures that make a website easier for search engines like Google to understand, technically sound, and relevant in terms of content, with the goal of appearing as high as possible in the results for relevant search queries. Unlike paid advertising, SEO does not produce immediate results, but it does deliver long-term benefits without ongoing click costs.

From our consulting experience: Companies that treat SEO as a one-time project usually lose the rankings they’ve gained within a short period of time. Lasting visibility can only be achieved through ongoing maintenance.

SEO can be broadly divided into three areas: on-page measures implemented directly on the website, technical optimization behind the scenes, and off-page signals such as backlinks from other domains. It is only through the interplay of these three areas that sustainable visibility is achieved. Anyone who views SEO solely as a technical checklist also underestimates its strategic component: Determining which search terms are truly relevant to your own offerings, which competitors are already ranking for them, and what content can fill that gap often has a greater impact on success than any single technical adjustment.

• Improves organic visibility over the long term
• Works without ongoing click costs
• Consists of on-page, technical, and off-page elements
• Requires ongoing care rather than a one-time project

SEO is particularly beneficial for small and medium-sized businesses because visibility is maintained regardless of the current advertising budget. An ad disappears the moment the budget runs out, whereas a well-positioned, organically ranked page continues to attract visitors over the long term without requiring payment for every single click.

How Search Engines Calculate Rankings

Google and other search engines evaluate each page based on several hundred signals and use them to calculate a ranking for each search term. Content relevance, technical quality, user signals, and external authority all play a role in this process. Since generative AI systems such as ChatGPT, Perplexity, and Google AI Overviews now formulate answers directly, the citability of such generative models adds a second layer of visibility to the traditional ranking system.

For businesses, it’s worth looking at both levels separately: Traditional ranking positions can be reliably tracked over a longer period of time using tools, while presence in generative responses has so far been more difficult to measure and can often only be verified on a random basis through manual test queries.

• Hundreds of signals are factored into the ranking
• Relevance, Technology, User Signals, Authority
• Citation impact complements traditional rankings
• Consider both levels together

The Key Components of Search Engine Optimization

On-page SEO encompasses all optimizations made directly on the page: structure, headings, internal linking, and the actual text optimization. Technical SEO works behind the scenes to ensure fast loading times, crawlability, and an error-free structure. Finally, off-page SEO builds a domain’s authority through backlinks and mentions, which is also central to E-E-A-T.

As part of on-page optimization, internal linking is just as important as the actual text: It helps search engines understand a website’s structure while also distributing authority from strong pages to weaker ones. In contrast, technically weak websites lose visibility even with excellent content if loading times are too long or if important pages are completely inaccessible to search engines.

• On-page: Structure, Text, Internal Links
• Technology: Load Time, Crawling, Error-Free Performance
• Off-Page: Backlinks and Authority
• All three components work together

SEO for Different Business Units

SEO doesn’t work the same way across all sectors. B2B companies target different search terms than consumer-facing online stores; HR departments struggle to gain visibility for their career pages through HR SEO; and individual job postings, in turn, follow their own technical rules on Google for Jobs. Video platforms like YouTube also have their own ranking algorithms, which require a separate YouTube SEO strategy.

These differences are particularly evident in vocabulary: While end customers often search using everyday language, technical decision-makers in the B2B environment use precise technical terms that require their own keyword research. Those who fail to use this language remain invisible to the relevant target audience, even if their website is technically flawless.

• B2B search terms vary widely
• HR SEO addresses career pages separately
• Job postings follow their own rules
• YouTube Needs Its Own Ranking Algorithm

For agencies, this diversity means that a single SEO measure rarely works equally well for all areas of a business. A career portal has different priorities than an online store, and a single job posting, in turn, follows its own technical rules rather than those of a permanent company website. In practice, those who treat these areas separately—rather than applying a single strategy across all page types—achieve significantly more reliable results.

SEO and Generative AI: The Shift to GEO

Generative AI models are changing the very nature of how visibility is created: Instead of a list of links, systems like Google AI Overviews provide a fully formulated answer with just a few cited sources. An SEO/GEO audit therefore checks—in addition to traditional technical factors—whether content is structured sufficiently to be cited by these AI systems. Those who address this early on secure visibility in a channel that is increasingly complementing traditional search results.

In practice, this means that content should answer questions in a way that makes the answer understandable even without additional context, with clear definitions at the beginning of a section rather than at the end of a long explanation. This structure helps not only generative models but also human readers who are looking for a quick answer.

• AI provides ready-made answers instead of lists of links
• Only a few sources are cited
• GEO-Audit Specifically Evaluates AI Citation Eligibility
• An early start secures an additional channel

SEO Projects: Strategy, Relaunch, and Ongoing Maintenance

A well-thought-out SEO approach begins with a clear SEO strategy that defines goals, priorities, and resources. When redesigning a website or migrating a domain, a clean SEO relaunch determines whether hard-earned rankings are preserved. During day-to-day operations, a regular content audit helps improve existing pages in a targeted manner, rather than focusing solely on producing new content.

These three phases are not a one-time process but rather repeat themselves over time: A relaunch is followed by a phase of strategic adjustment, and an audit is followed by a phase of implementation. Companies that plan for this cycle are less likely to find themselves in a situation where they suddenly have to react to declining figures.

• The strategy sets goals and priorities
• Relaunch safeguards existing rankings
• A content audit improves existing pages in a targeted manner
• All three phases are interrelated

An Overview of All In-Depth SEO Articles

There are detailed articles dedicated to each specific area of SEO. The following overview provides links to all the key topics:

• HR SEO: Making Career Pages Visible on Google
• B2B SEO: For Decision-Makers, Not End Customers
• Technical SEO: Load Time, Crawling, Structure
• SEO Strategy: Long-Term Visibility Planning
• SEO Relaunch: Maintaining Rankings During a Site Move
• SEO/GEO Audit for Google and AI Visibility
• Job Posting SEO for Google for Jobs
• YouTube SEO: Channel Growth and Reach
• On-Page SEO: Ranking Factors on Your Own Website
• SEO Text Optimization for Better Rankings
• Content Audit: Analyzing and Improving Content

In addition, the glossary entries on ” domain,” ” ranking,” ” generative models,” ” E-E-A-T,” and ” FAQ” explain the most important basic concepts related to SEO.

SEO is an ongoing process

SEO rarely delivers results overnight, but it does provide visibility that lasts over the long term and doesn’t disappear once the advertising budget runs out. Anyone who understands basic concepts such as domain, ranking, and E-E-A-T and presents questions clearly in an FAQ format lays the foundation for all further measures.

If you also keep an eye on external factors such as backlinks and how you address your target audience, you’ll cover virtually all the relevant levers. SEO thus remains a discipline that requires patience, but unlike short-term advertising measures, it continues to deliver visibility even when the budget for other channels is temporarily suspended.

For companies looking to build an SEO strategy from scratch or revamp an existing one, it’s advisable to set a realistic timeframe: The first measurable results often become apparent after a few months of consistent effort, while noticeable improvements for highly competitive keywords often take significantly longer. By factoring this timeframe in from the start, you can avoid unmet expectations and evaluate individual measures more fairly, rather than hastily questioning the entire strategy after only a short time.

• The effect develops over a longer period of time
• Understand and Apply the Basic Concepts First
• Consistent implementation is more important than speed
• The foundation supports all subsequent steps

What are generative AI models?

Generative models are AI systems that have been trained on massive amounts of text and use that data to formulate new, coherent responses rather than simply listing existing documents. They recognize patterns in language and use them to determine the probability of which word would make sense next.

It’s important to understand that while a generative model does not intentionally invent facts, it can make mistakes. Clear, unambiguous source information is incorporated much more reliably than vague statements.

Well-known examples include chat systems like ChatGPT, answer engines like Perplexity, and Google AI Overviews, which are integrated directly into the search function. All three also access web content to answer current questions, citing selected sources in the process. Such models are trained in several stages: First, they learn general language patterns from a very broad text corpus; then, they are specifically trained to formulate helpful, reliable, and transparent answers. This second step plays a decisive role in determining how a model handles uncertain or contradictory information.

• Formulate your own, new answers
• Trained on massive amounts of text
• Access the Web for the latest information

A Comparison of ChatGPT, Perplexity, and Google AI Overviews

The three systems differ significantly in how they are used and in their approach to sourcing information. ChatGPT primarily answers questions based on its training data and supplements its responses with web searches only when necessary. Perplexity was designed from the outset as an answer engine with visible source lists. Google AI Overviews appear directly above the traditional search results and primarily draw on content that already ranks highly.

It is important for businesses to note that these systems differ significantly in terms of how up-to-date they are: Some access the web in real time for every query, while others rely primarily on a body of knowledge that was “frozen” at a specific point in time and update it only incrementally. Anyone relying on up-to-date content—such as prices, availability, or events—should therefore know which system their target audience actually uses.

• ChatGPT: Knowledge plus targeted web searches
• Perplexity: Answer Engine with a List of Sources
• AI Overviews: Right in Google Search
• Everyone clearly prefers well-structured sources

Relevance for Generative Engine Optimization

Because generative models cite only a few sources per answer, a new competition for visibility is emerging within the AI response itself. Content that answers questions precisely, in a structured manner, and with clear definitions is given preference. For businesses, this means that traditional on-page optimization alone is no longer sufficient; a clean text structure is also essential for machine-readable answers.

In addition to the text structure itself, technical accessibility also plays a role: Content that can be reliably read by crawlers—without important information being hidden behind interactions such as clicks or forms—is captured and reproduced correctly by generative systems significantly more often.

• Few sources per AI response
• Precise, well-organized answers are preferred
• Clear definitions increase the likelihood of being cited
• On-page SEO remains a basic requirement

Understanding Generative Models Correctly

Despite all the attention they’ve received, generative models have not yet completely replaced traditional search; rather, they add an additional layer to it. Many users consciously switch between a traditional search engine and a chat system depending on their query—whether they need a quick, direct answer or multiple sources for comparison. For businesses, this means managing both forms of visibility in parallel, rather than focusing on just one.

• They complement traditional search; they do not replace it
• Users change depending on the request
• Manage both forms of visibility simultaneously
• Technical accessibility remains a prerequisite

Understanding Generative Models: Common Misconceptions

A common misconception is the assumption that generative models search the internet directly, like a traditional search engine. In fact, many systems combine trained knowledge with selective, targeted web searches, which means that not every query automatically includes current web content. Those who aren’t aware of this may be surprised by outdated or incomplete answers.

The origins of the models are also often confused: Not every system that appears to be a standalone product comes from the company of the same name. The article on OpenAI —one of the best-known providers behind ChatGPT—provides some background on this.

• Not every query uses live web search
• Training information may be outdated
• Do not confuse the provider and the model name
• Check a system’s origin in advance

Preparing Content for Generative Models: Step by Step

If you want to tailor content specifically for generative systems, it’s best to start with a content audit of existing pages to identify which pieces of content are already clearly structured and which need to be revised. The next step is to revise individual sections into precise, self-contained answers to specific questions.

Finally, it’s worth conducting a technical check to see whether this content is even accessible to crawlers before continuing to work on the content of other pages.

• Audit existing content first
• Rewrite sections to provide clear answers
• Perform a final check of technical accessibility
• Take it step by step rather than all at once

What is a search engine ranking?

Ranking refers to the order in which a search engine displays websites in response to a specific search query. For each search term, every URL is assigned its own position, which is determined by hundreds of individual signals and can change with every algorithm update.

From real-world experience: Rankings are never set in stone; they’re just a snapshot in time. If you don’t check them often enough, you’ll miss shifts that could otherwise be addressed early on.

Google calculates this ranking slightly differently for each user, location, and device, which is why two people performing the exact same search rarely see the exact same order of results. For this reason, rankings are typically tracked using aggregated tools rather than through Google Search itself. In addition, personalized search histories add to the uncertainty: Two identical queries can yield different result lists depending on the time of day, previous search behavior, and device type. Anyone who wants to evaluate changes in rankings should therefore always consider a sufficiently large sample of search terms rather than individual snapshots.

• Rank per search term and page
• Changes with every update
• Varies by location and device

An Overview of the Most Important Ranking Factors

According to Google, it uses several hundred signals that can be broadly grouped into four categories: content relevance, technical quality, user signals, and external authority. A strong piece of content without a solid technical foundation ranks just as poorly as a fast-loading page without relevant content. It’s always the interplay of these factors that matters, not any single factor on its own.

Within the four groups, the weightings also shift depending on the search intent: For information-oriented queries, content depth carries more weight, while for transactional queries with the intent to purchase, trust signals, reviews, and a seamless user experience take center stage. Those who ignore these differences may end up optimizing the wrong aspects.

• Relevance and Depth of Content
• Technical Loading Time and Availability
• User signals such as time spent on the site, click-through rate
• External Authority Through Backlinks

Traditional Ranking vs. GEO Citation Index

Traditional ranking is optimized for a position in a list of blue links. Generative AI systems, such as chatbots and AI summaries, on the other hand, provide a direct, formulated answer that cites only a few sources. For this new form of visibility, the position is less important than whether one’s own page is recognized as a citable source at all and included in the answer.

This shift does not mark the end of traditional rankings, but rather an expansion: For many search queries, both types of results exist side by side—the traditional list of links and an AI-generated summary of those results. Websites that appear in both formats gain significantly more visibility than competitors that appear in only one of the two formats.

• Ranking shows position in the results lists
• Citation Potential Determines AI Responses
• Clear facts are more likely to be cited
• The two forms are increasingly complementing each other

Improve Your Ranking Long-Term

Sustainable improvement doesn’t come from isolated tricks, but from continuous work on content, technical aspects, and authority. A structured analysis of your current site often reveals potential more quickly than new articles, because existing pages frequently gain rankings immediately with just minor adjustments. Those who also regularly check on-page factors can prevent any ranking gains from being lost again.

Ongoing monitoring is just as important as new measures: By tracking ranking trends across multiple search terms, you can identify patterns—such as seasonal fluctuations or the impact of individual Google updates—much earlier than you would by simply examining individual rankings on an ad hoc basis.

• Revise existing pages regularly
• Continuously monitor on-page factors
• Build Authority Through Genuine Backlinks
• Monitor developments over an extended period of time

Measuring Rankings Accurately: Common Mistakes

A common mistake is checking your own ranking using a personal Google search in your browser, because the results are skewed by your location, search history, and device. If you want to seriously monitor your own ranking, you need neutral, location-independent metrics rather than a single, random glance at your own search results.

Similarly, rankings are often viewed in isolation for a single keyword, even though a page is usually visible for many related terms at the same time. A sound keyword strategy therefore takes into account entire topic clusters rather than individual, isolated terms.

• Do not check using your own Google search
• Objective Measurements Instead of Lucky Guesses
• Focus on topic clusters rather than individual keywords
• Consider Related Search Terms

How Backlinks and External Signals Support Rankings

In addition to content and technical factors, external authority remains a key component for stable search rankings. Backlinks from thematically relevant, trustworthy sites have a significantly greater impact than a large number of random, unspecific links.

Anyone looking for additional professional support should carefully evaluate whether an SEO agency offers sustainable link-building strategies or short-term, risky methods, since the latter are more likely to jeopardize rankings in the long run than to strengthen them.

• Give preference to thematically relevant backlinks
• Quantity Does Not Equate to Relevance
• Prioritize sustainable link-building strategies
• Avoid Risky Methods in the Long Term

What Is GEO? Definition and How It Differs from Traditional SEO

Generative Engine Optimization Explained Simply

Generative Engine Optimization refers to the targeted optimization of content so that it is cited, paraphrased, or referenced as a source in AI-generated responses. The difference from search engine optimization lies not in the goal—both aim to build visibility and trust—but in the mechanism. Traditional search engines provide a list of links from which users make their own selections. Generative systems such as ChatGPT or Perplexity, on the other hand, deliver a ready-made synthesis of multiple sources, deciding for themselves which statement to take from which website.

For businesses, this means that a page can be technically optimized for Google to a high standard and still not appear in a single AI response because it lacks the structure, conciseness, or verifiability that a language model needs for a citation. GEO is therefore not a subset of SEO, but a parallel field of expertise with its own rules, its own success metrics, and its own technical requirements.

Why ChatGPT, Perplexity, and Others Need Their Own Rules

Generative search systems operate using retrieval mechanisms that break down content into small, meaningful units rather than treating an entire page as a ranking entity. A paragraph, a definition, or a row in a table can be cited, even if the rest of the page is irrelevant to the model. This shifts the focus of optimization from the domain and page level to the paragraph and statement level—a way of thinking that many companies are unfamiliar with when it comes to traditional link building and domain authority.

In addition, each system applies its own weightings. Perplexity places visible emphasis on recently crawlable, clearly cited sources and displays citations transparently. Google AI Overviews relies more heavily on highly trustworthy domains already established in the traditional index. ChatGPT with web access combines both approaches, favoring content that can be used directly as a response without rephrasing. A GEO agency must be aware of these differences rather than optimizing content across the board for “AI.”

Ranking Signals in Generative AI Search Systems

Citation Potential Instead of Click-Through Rate: The New KPI Logic

In traditional SEO, what matters is how often a page is clicked on in search results. In GEO, what matters is how often a brand is mentioned by name or as a link in a generated response—regardless of whether that results in a click. This shift is fundamental because it doesn’t replace traditional success metrics based on Search Console and click-through rates, but rather complements them. An agency that takes GEO seriously needs additional monitoring methods to even make mentions in AI responses visible.

How Language Models Select and Weight Sources

Generative models favor content that is clearly formulated, verifiable with current evidence, and consistent with other trustworthy sources. If a statement is consistently repeated across multiple independent, thematically relevant pages, the likelihood of it being adopted increases. Importantly, the inclusion does not have to be verbatim. Models often paraphrase content but retain key terms, numbers, and the structure of the argument—those who clearly highlight these core elements significantly increase the chance of being cited, even if their own wording does not appear exactly as written in the end.

The following table compares the key differences between traditional SEO and GEO:

Content formats cited by AI systems

Structure, Extractability, and Semantic Clarity

Generative models tend to extract text snippets that are self-contained and understandable without additional context. This favors certain formats over others:

• Explain the term precisely in two sentences
• Comparison tables can be imported directly
• Numbered Instructions as Clear Process Knowledge
• Bold text plus a brief explanation provides citation blocks
• Q&A sections increase the likelihood of conversion
• Verifiable figures are more credible

Those who are already working on a creative content marketing strategy or a multi-step content marketing approach often already have the editorial foundation for GEO—it just needs to be supplemented with the structural elements mentioned above.

E-E-A-T and Authorship as a Sign of Trust

Experience, expertise, authority, and trustworthiness play at least as big a role in GEO as they do in traditional SEO—generative models are increasingly being trained to place greater weight on author signals, expert references, and verifiable citations than on anonymous, interchangeable text. A clear byline, a transparent company profile, and consistent expert statements across multiple pages pay off twice: once for Google, and once for the generative layer on top of it.

An often underestimated side effect: Content with clear authorship and verifiable depth of expertise ages better. While superficial, interchangeable how-to articles are quickly supplanted by newer, equally superficial content, well-researched definition pages with reliable citations remain in circulation as references over long periods of time—both in traditional indexes and in the training and retrieval foundations of generative systems.

GEO Strategy: The Building Blocks of Successful Implementation

For a brand to be cited in AI responses, it must first be findable, unique, and consistent within the models’ training and retrieval data—visibility in generative search doesn’t start with the prompt, but with the quality and consistency of the brand’s own content.

Technical Fundamentals: Crawling, Structured Data, Sitemaps

Before a language model can cite content, it must be able to reliably crawl and understand it. This includes clean sitemaps and a functional crawling infrastructure, a logical URL structure, and structured data (Schema.org) that machine-readably marks up entities such as organization, author, FAQ, or product. Technical basics such as appropriate WordPress plugins for structure and load time also indirectly contribute to GEO, because many retrieval systems use the same technical signals as traditional search engine crawlers.

Content Clusters and Entities Instead of Individual Keywords

Instead of optimizing for individual keywords, GEO works with thematic clusters centered around an entity—a brand, a product, or a technical term. The goal is for a model to consistently associate the brand with a specific topic, similar to a well-maintained satellite project within one’s own network that independently covers a topic from multiple perspectives. This is complemented by classic yet targeted strategic link building, which signals consistency and authority across multiple external sources—a factor that Google’s algorithm updates are increasingly prioritizing anyway.

Monitoring: Measuring Visibility in AI Responses

Since traditional analytics tools do not track citations in ChatGPT or Perplexity, GEO needs its own monitoring system: regular, standardized prompt tests on its own core topics, a comparison of the sources cited with its own benchmarking against competitors, and ongoing monitoring of which phrasing is actually adopted. Related topics such as virtual influencers and AI avatars —or AI avatars in advertising campaigns —also demonstrate how deeply generative systems have now penetrated brand communication —GEO is not a peripheral issue here, but rather a necessary complement to the overall brand strategy.

Checklist: GEO Optimization for Your Own Website

A reputable GEO agency systematically addresses the following points rather than simply tweaking individual text blocks. The order is deliberately chosen: Without technical crawlability and consistent messaging, even the best definition paragraph won’t result in a citation, because the model simply cannot reliably capture the content.

• Every technical term explained in a way that makes it easy to cite
• Schema markup is complete for all types
• Ensure that facts and figures are consistent
• Real authors with verifiable expertise
• Sitemap, robots.txt, and load time are all in order
• Check regularly to see if the brand appears
• Link related content to each other in a meaningful way

Frequently Asked Questions About Generative Engine Optimization

Does GEO completely replace traditional SEO?

No. GEO does not replace SEO; rather, it complements it by adding an additional layer of optimization. A website still needs a solid technical foundation, a clean on-page structure, and a strong backlink profile to be perceived as a trustworthy source in the first place. GEO builds on this foundation and ensures that precisely this trustworthy content is recognized and cited by generative systems.

How long does it take for a website to be cited in AI responses?

This depends heavily on the subject area, the level of competition, and the quality of existing content. As a general rule, the more consistently and verifiably a brand is already represented across multiple online sources, the faster generative systems will respond to new, well-structured content. Realistically, you should plan for a continuous process spanning several months, not a one-time project.

Does every company need its own GEO strategy?

Not every company needs a comprehensive GEO strategy right away, but any company with significant brand recognition or consulting services should at least review how it is currently represented in generative responses. If a brand is mentioned incorrectly, incompletely, or not at all, while competitors are cited, this creates a visibility disadvantage that can no longer be remedied by traditional SEO alone. Therefore, industries that are particularly consulting-intensive and where trust is a key factor should not treat GEO as an optional extra, but rather as an integral part of digital brand management alongside traditional search engine optimization, social media, and content production.

To clarify: This audit is not a content audit, which evaluates the performance of individual articles, nor is it a social media audit, which analyzes channels and interactions. An SEO/GEO audit delves one level deeper: technology, structure, crawlability, and the authority of the entire domain—the foundation upon which any content or social media strategy can be built.

What an SEO/GEO Audit Is and Why Traditional SEO Alone Is No Longer Enough

An SEO/GEO audit is a systematic review of all technical, structural, and content-related factors that determine, first, whether a website is crawled, indexed, and ranked by Google, and second, whether it is recognized and cited as a source by AI systems. These two goals overlap significantly but differ in important details—and it is precisely these details that will determine who remains visible online in the future.

Traditional Visibility vs. AI Visibility

Traditional SEO is optimized for a click: A page is intended to appear in a list of results and encourage a user to visit it. GEO is optimized for a citation: A paragraph or a single fact is to be extracted from the text by a language model and reproduced in a generated response—with or without a link to the source. This changes how content must be structured, phrased, and marked up.

For companies, this means two things: First, visibility is no longer lost solely due to poor rankings, but also because an AI response might cite a competitor while failing to mention the company’s own brand. Second, this new form of visibility can be systematically assessed and improved once it becomes clear which technical and editorial factors are responsible for it. The audit identifies precisely these factors.

Why ChatGPT, Perplexity, and AI Overviews Are Enforcing New Rules

AI systems prefer clearly defined, self-contained blocks of text with verifiable statements, clean semantic markup, and traceable sources. A page that ranks perfectly from a technical standpoint but hides its key messages in nested paragraphs of continuous text will simply be overlooked by a language model. The audit reveals precisely this gap between “rankable” and “citable.”

A professional SEO/GEO audit examines these areas

The scope of the audit is divided into four core areas that are interdependent. If one is missing, the overall result remains incomplete—a technically perfect page with a weak backlink profile will rank just as poorly as a well-linked page with broken crawlability.

Technical SEO and Crawlability

This section checks whether search engines and AI crawlers can fully crawl the website at all: robots.txt,XML sitemap, canonical tags, status code distribution, indexing status in Google Search Console, and the depth of internal linking. The URL structure also falls under this category, because descriptive, consistent paths help both crawlers and language models categorize content by topic.

Content Structure for AI Citation

This section evaluates whether content is formatted in a way that allows a language model to extract it: a clear H2/H3 hierarchy, questions answered in separate paragraphs, definitions at the beginning of paragraphs rather than at the end, tables and lists for comparable data, and structured data (Schema.org) for FAQs, How-To, and Article. Text optimization using E-E-A-T signals is also factored in here, as language models place a high value on recognizable authorship and subject matter expertise.

Backlink Profile and Core Web Vitals

Backlinks remain a key authority factor—both for Google rankings and as a signal to AI systems indicating which domains are considered trustworthy. The audit evaluates link quality, anchor text distribution, broken backlinks, and toxicity, among other factors, using a backlink network approach. At the same time, Core Web Vitals are measured: load time, layout stability, and interactivity, because a slow page wastes both user time and crawler budget.

• Indexing Status in Google and AI Data
• Crawl Budget: Efficiency of Bot Activity
• Duplicate content dilutes ranking signals
• Schema Markup for FAQs, Articles, and Products
• Check Mobile Display and Usability
• Check Thematic Clusters and Link Flow
• Citation and Verifiability of Key Statements
• Author Award for Demonstrated Expertise

Here’s How an SEO/GEO Audit Is Conducted at the Agency

The process follows a fixed sequence because each phase builds on the results of the previous one. Without a complete data collection, it is impossible to establish a reliable set of priorities, and without those priorities, every report remains nothing more than an unsorted list of errors.

Steps 1 and 2: Data Collection and Technical Crawl

First, the entire domain is crawled in its entirety—status codes, metadata, internal links, and load times. At the same time, Google Search Console, Google Analytics, and a backlink tool such as Ahrefs or Sistrix are integrated to consolidate ranking, traffic, and link data. Additionally, we check how and whether the domain currently appears in responses from ChatGPT, Perplexity, and Google AI Overviews.

Steps 3 and 4: Analysis and Prioritization

All collected data is compared against a checklist consisting of several hundred individual items. The result is not merely a list of errors, but a prioritized list sorted by effort and expected impact. Quick wins with high impact and low effort—such as missing canonical tags or unstructured FAQ sections—are separated from long-term structural projects, such as a complete overhaul of the information architecture.

It is important to distinguish between technical findings—which are usually addressed by a development team—and editorial findings, which are incorporated into content planning. An audit that conflates these two levels overwhelms teams and delays implementation. A clear assignment of responsibilities is therefore just as much a part of the outcome as the technical analysis itself.

An audit that merely lists errors is a report. An audit that categorizes errors by impact and effort is a roadmap.

A Comparison of Traditional SEO and GEO

The following overview shows where the evaluation criteria overlap and where they differ. Anyone familiar with both columns will understand why a standalone SEO check is no longer sufficient today.

What the customer receives as a result: a report and a priority list

The end result is not just a jumble of numbers, but a clear document that is easy to understand even without SEO expertise. It serves as a direct working basis for developers, editorial staff, and management alike.

Structure of the Audit Report

The report is typically structured into an executive summary, a detailed analysis for each audit area—including screenshots and supporting documentation—and a clear list of priorities with estimated effort, expected impact, and assigned responsibility. In addition, a comparison of visibility within the relevant competitive landscape provides the necessary context.

Prioritization Based on Effort and Impact

Each finding is classified into one of three categories: immediate action, medium-term project, or strategic groundwork. This structure prevents companies from getting bogged down in minor details while key structural issues remain unaddressed—for example, following a major Google algorithm update that affects entire categories of websites.

Checklist: Preparing for an SEO/GEO Audit of Your Own Website

Before your first meeting with an agency, it’s worth taking a few minutes to prepare internally. You can check these points yourself without any special tools, and doing so will significantly speed up the audit later on.

• Grant Access to Search Console, Analytics, and the CMS
• Check the submitted XML sitemap
• Check robots.txt for blocked pages
• Test the brand on ChatGPT and Perplexity
• Measure Load Time Using the Core Web Vitals Tool
• Collect previous audits and evaluations
• List three to five competitors

Clarifying these points in advance significantly shortens the data collection phase and ensures that the audit can focus directly on the content analysis. For companies with a regional focus, it’s also worth taking a look at existing articles such as SEO recommendations for on-page and off-page optimization or the overview of SEO agencies for common CMS systems to better assess their own starting point. The article on on-page optimization and ranking factors also provides in-depth background information on individual checkpoints. The article on AI overviews and Gemini in Google Search illustrates just how much the rules have already shifted due to generative search.

Frequently Asked Questions About the SEO/GEO Audit

How long does a complete SEO/GEO audit take?

Depending on the size of the website, the analysis phase alone takes between one and three weeks. Larger domains with several thousand URLs or multilingual structures require correspondingly more time for crawling, data reconciliation, and creating the priority list.

Is a GEO audit technically very different from a traditional SEO audit?

The technical foundation is largely the same, as both are based on crawlability and indexability. The difference lies in the additional evaluation of text structure, citability, and structured data, which are particularly relevant for extraction by language models.

Is a one-time audit sufficient, or does it need to be repeated?

A one-time audit lays the groundwork but does not replace ongoing monitoring. Since Google algorithms and AI models are constantly changing, it is advisable to re-evaluate key areas at regular intervals, especially after major website changes or relaunches. Many agencies therefore offer a streamlined follow-up audit that re-examines only the previously identified problem areas and highlights progress.