In traditional SEO, authority is built through backlinks. A link from a relevant site is a vote of confidence that Google interprets as a quality signal. For over two decades, link building has been the backbone of any organic ranking strategy.
But AI-powered search engines don't work that way.
ChatGPT, Perplexity, Claude, and Google AI Overviews don't crawl your backlink profile to decide whether to cite you. They use a completely different mechanism to evaluate your authority: co-citations.
And this is where a new concept emerges: AI Building.
What Is AI Building?
AI Building is the discipline of building perceived authority by language models (LLMs) through the analysis and optimization of your co-citation neighborhood.
If traditional link building asks "Who links to my website?", AI Building asks: "Who does the AI cite alongside me when answering questions about my industry?"
When a user asks ChatGPT "What's the best flooring for a garage?", the model generates a response that may mention several domains. If your site appears alongside recognized authority sources in your sector (manufacturers, trade publications, expert guides), the LLMs reinforce your position as a reliable source. If you appear alongside generic directories or low-quality sites, your perceived authority erodes.
That set of domains that AI associates with you is your co-citation neighborhood, and optimizing it is the essence of AI Building.
From Backlinks to Co-citations: Why the Model Changed
Traditional SEO is based on a link graph. Each backlink is an edge connecting two nodes (websites), and Google uses algorithms derived from PageRank to distribute authority through that network.
LLMs operate under a different paradigm. When a model generates a response, it queries its training data and, in many cases, performs real-time searches (RAG). The model synthesizes information from multiple sources and cites them together in the same response.
This process creates a new type of relationship between websites: AI co-citation.
A co-citation occurs when two or more domains appear mentioned in the same LLM response to a specific query. Unlike a backlink, which is a direct relationship between two sites, co-citation is a contextual relationship created by the model: the AI has decided that both domains are relevant to the same question.
And this has profound implications.
The correlation between co-citation and perceived authority
Recent research on LLM citation patterns reveals significant data. According to industry findings, brand search volume has a 0.334 correlation with LLM citations, far exceeding backlinks as a predictive signal. Sites cited across 4 or more AI platforms are 2.8x more likely to be consistently mentioned by ChatGPT.
In other words: LLMs care less about who links to you and more about who they associate you with.
How the Co-citation Neighborhood Works
Imagine you run an online flooring store. When a user asks ChatGPT "What type of flooring is best for a residential garage?", the model might cite three domains in its response: yours, a well-known manufacturer, and a home improvement portal.
Those three domains form your co-citation neighborhood for that specific query. But your neighborhood changes depending on the question, the platform, and the timing.
AI Building focuses on analyzing these patterns to answer questions like:
Who are your most frequent co-citation neighbors? These are the domains that AI repeatedly associates with you. If they're high-authority sites in your sector, your position is strong. If they're direct competitors, you're in a visibility battle.
What position do you appear in? Being the first domain mentioned isn't the same as being third. Position within the LLM's response reflects perceived relevance.
How does it vary across platforms? ChatGPT, Perplexity, Claude, and Gemini have different citation behaviors. ChatGPT prioritizes encyclopedic authority, Perplexity favors recent content and discussions, and Google AI Overviews tends to cite diversified sources.
Are there strategic link building opportunities? If a domain consistently appears as your co-citation neighbor but doesn't link to you, it's a natural outreach candidate.
AI Building vs. Link Building: A Comparison
To better understand the differences, let's compare both disciplines side by side.
Link building focuses on getting other sites to link to you. The primary metric is the number and quality of inbound backlinks. The mechanics are based on the web's link graph (PageRank), where a link equals a direct vote of confidence. Impact is measured in Google rankings.
AI Building focuses on appearing alongside authority sites in LLM responses. The primary metric is your co-citation neighborhood and your position in responses. The mechanics are based on language models' synthesis patterns, where a co-citation equals a contextual association created by AI. Impact is measured in visibility across ChatGPT, Perplexity, Claude, and Google AI.
Importantly, they are not mutually exclusive. A strong backlink profile remains relevant because LLMs with real-time search (RAG) use Bing and Google results. But backlinks alone aren't enough: you need LLMs to associate you with the right sources.
Practical AI Building Strategies
Now that we understand the concept, let's see how to apply it.
1. Analyze your current neighborhood
Before optimizing, you need to know where you stand. Run the same queries your customers would across ChatGPT, Perplexity, Claude, and Gemini. Record which domains appear alongside yours and how frequently. Identify patterns: do you always appear next to the same competitors? Are there authority sites you're never co-cited with?
2. Identify your ideal neighbors
Not all co-citation neighbors are equal. Your ideal neighbors are high-authority sites in your sector that aren't direct competitors: manufacturers, industry associations, specialized media, reference portals. Appearing alongside them reinforces your own perceived authority.
3. Create content that AI can synthesize
LLMs cite content that's easy to extract and synthesize. This means structuring your pages with direct answers to specific questions, using clear headings and verifiable data, including structured data (Schema) so AI crawlers understand your content, and keeping content fresh (LLMs prioritize recent sources).
4. Strategic LLM Seeding
The concept of LLM Seeding involves "planting" information about your brand in sources that LLMs frequently consult. This includes participating in Reddit and specialized forums with helpful contributions, getting mentions in publications that LLMs already cite, creating original data and studies that others want to reference, and maintaining consistent presence on review platforms and directories.
5. Monitor and adjust
AI Building isn't a one-time action. Co-citation patterns change constantly as models update and new content enters the picture. You need continuous monitoring to detect changes in your neighborhood, identify new opportunities, and react when a competitor displaces you.
How to Measure Your AI Building Authority
Traditional SEO metrics (DA, DR, backlink count) don't reflect your authority in the AI ecosystem. For AI Building, the key metrics are:
Co-citation frequency: How often do you appear in LLM responses for your target keywords? More presence equals greater recognition.
Neighborhood quality: Are the domains that appear alongside yours high-authority or low-quality? A premium neighborhood elevates your perception; a generic one erodes it.
Position in response: Are you the first source mentioned or the last? Position reflects the priority the model assigns to your content.
Multi-platform coverage: Do you appear in ChatGPT, Perplexity, Claude, and Google AI, or only one platform? Cross-platform presence indicates more solid authority.
Co-citation sentiment: Is the context in which you're mentioned positive, neutral, or negative? It's not enough to appear; how you appear matters.
AI Building with LLMFY
At LLMFY, we've built the first tool that lets you systematically analyze and optimize your co-citation neighborhood.
Our AI Building feature directly queries major LLMs (ChatGPT, Claude, Perplexity) with your target keywords and analyzes which domains appear alongside yours in each response. It shows you your co-citation neighbor map, your AI authority score per keyword, link building opportunities based on co-citation, and the evolution of your neighborhood over time.
Combined with our Prompt Tracker, which analyzes your visibility across 5 LLM platforms simultaneously, and LLM Tracking, which monitors your brand mentions in AI, you have a complete suite to dominate AI-powered search.
Conclusion: The Future of Positioning Is Contextual
Link building isn't dead, but it's no longer enough. In a world where billions of people get answers directly from ChatGPT, Perplexity, and Google AI, authority isn't just measured by who links to you, but by who the AI associates you with.
AI Building is the natural evolution of link building for the era of language models. And the SEO professionals who understand it first will have a competitive advantage that's hard to replicate.
The question is no longer "How many backlinks do you have?". The question is: "Who does the AI cite you alongside?"
Want to analyze your co-citation neighborhood? Try LLMFY for free and discover which domains AI associates with yours.
JesusLopezSEO
Founder & CEO at LLMFY · 18+ years in SEO
SEO expert with over 18 years of experience. Pioneer in LLMO (Large Language Model Optimization) and founder of Posicionamiento Web Systems. Helping companies optimize their presence in traditional search engines and AI search engines.
