AI SEO: Strategies to Optimize Visibility on LLMs & AI Search

According to a study published by the Capgemini Research Institute, 73% of consumers trust content produced by generative AI tools like ChatGPT. This underscores consumers' growing trust in AI-generated content while highlighting the importance for brands to optimize their visibility within AI search platforms.

As AI search tools like Perplexity, ChatGPT, and Google’s Gemini become more prominent, traditional SEO strategies are evolving. These platforms don’t just provide users with links; they summarize, synthesize, and often replace the need to click through. Brands that don’t adapt to changing search behaviors risk becoming invisible in AI answer engines, even if they rank well on traditional search engines like Google. 

Visibility is shifting from “10 blue links” to being the trusted source AI pulls from.

What is AI SEO?

AI SEO refers to the practice of optimizing content, websites, and other digital assets to increase visibility and discoverability within AI search platforms and large language models (LLMs), like ChatGPT, Google’s AI Overviews, Perplexity, Claude, and many others. 

While traditional SEO focuses on ranking in search engine result pages (SERPs), AI SEO is about being referenced in AI responses. This means training LLMs to recognize your content as authoritative and structuring information for easy understanding, retrieval, and repurposing by machines. 

You might think AI SEO refers to using AI tools to perform SEO tasks, but in this context, it’s about aligning your content with the needs of AI systems that generate answers, ensuring your content is easy for these systems to interpret, trust, and present. 

AI SEO involves a blending of semantic optimization, content structuring, entity building, and monitoring AI visibility across platforms. To sum it up, AI SEO is about preparing your content for a search realm where answers matter more than rankings, and your visibility depends on how each AI model perceives, understands, and trusts your content.

Remember that AI SEO isn’t meant to replace traditional SEO methodologies; it builds on them. But as search behavior shifts toward AI for information discovery, visibility depends on being part of the answer, not just the list. 

How AI is Changing the Rules of Search

The rise of AI search platforms with browsing capabilities has fundamentally altered how people discover and interact with information. While traditional SEO focuses on climbing the SERP ladder to claim a top blue-link spot on the first page, the new paradigm is about earning a reference in an AI response. 

From Traditional Search to Generative AI

In the past, search success meant securing a top position on page one. But AI searches don’t always present a list of links in response to a user’s query. Instead, they generate direct answers, often synthesizing from multiple sources, and they may never show the original links at all. This means your goal isn’t just to rank, it’s to be cited by AI as a credible source. 

This shift requires content creators and SEO professionals to rethink optimization. It’s about:

• Creating content that is factually correct, clearly structured, and semantically rich. 
• Using structured data like schema markup to help machines interpret and extract your content. 
• Building topical authority, so LLMs recognize your domain as trustworthy while generating summaries. 

The New Information Landscape

At its core, AI reshapes how people find information by: 

• Reducing the number of clicks. Meaning, users increasingly are getting the information they need without ever having to visit a website. 
• Answering questions contextually. AI understands nuance, intent, and the connection between related queries, allowing for a more fluid, conversational search experience. 
• Introducing discovery paths. Voice search, image-based queries, and multimodal prompts (text + image + follow-up questions) are becoming more common. 

As users shift from clicking to asking, the rules of SEO are being rewritten. Winning brands aren’t just those that top the SERP – they’re the ones AI platforms consistently surface as trusted sources in conversational answers. 

How LLMs Process and Surface Content

To optimize your website for AI search visibility, you need to understand how LLMs process and surface content. LLMs like those powering ChatGPT and Perplexity AI don’t rank content the same way traditional search engines do. Instead, they use a combination of various factors such as semantic understanding, source reliability, and contextual relevance to decide what information to include in their responses to user queries. 

Here’s a breakdown of how LLMs process and surface content, with an emphasis on the seven key steps involved in surfacing brands: 

1. Understand the Query: Intent and Entity Detection

The first step is understanding the user’s query. LLMs work to identify: 

• User Intent: Whether the user is looking to buy, compare, learn, or gather information. 
• Entities: Key elements such as products, brands, categories, and topics. 
• Personalization (if available): LLMs may consider past behavior or preferences when available.

2. Retrieve Relevant Information: Leveraging Search Indexes and APIs

Once the query is understood, LLMs retrieve relevant information from various sources. Including: 

• Search Indexes: Web content indexed for broad access. 
• APIs and Internal Knowledge (e.g., Retrieval-Augmented Generation – RAG): AI tools that pull in real-time data from web searches to enhance accuracy and freshness.  
• Sources: Brand websites, product pages, reviews, articles, etc. 

3. Score and Rank Sources: Determining Relevance and Authority

LLMs score and rank sources based on a variety of factors, which we’ll dive into later in this article. Here’s a brief introduction to this important step of the process: 

• Relevance to the Query: How closely the content matches the user’s intent. 
• Authority and Trustworthiness: LLMs assess the reliability of the source (e.g., authoritative brands or websites).
• Freshness and Recency: More recent content may be prioritized. 
• Sentiment: Positive or negative sentiment in the content may impact rankings. 

4. Entity Linking: Accurate Representation of Brands

When LLMs surface content, they ensure that entities (e.g., brands) are represented accurately. This includes entity recognition, which implies linking mentions of brands (e.g., “Nike” → Nike, Inc.) and ensuring there aren’t duplicates or inconsistencies in the data. 

5. Generate Answer: Synthesis of Information

After retrieving the data, LLMs generate an answer by synthesizing content from multiple sources:

• They prioritize useful, trustworthy, and relevant information.
• The response might include summaries, lists, or combinations of insights from various content sources. 

6. Rank Brands in the Response: Relevance and Quality

The LLM then ranks brands or information based on:

• Relevance to the Query: Whether the brand is the best match for the user’s intent. 
• Sentiment and Data Quality: Positive sentiment and clear, authoritative content increase the likelihood of higher placement. 

7. Apply Output Filters: Ensuring Safety and Accuracy

Finally, LLMs apply filters to ensure the response meets safety standards and avoids issues like:

• Brand Safety: Ensuring that no harmful content surfaces. 
• Hallucinations: Ensuring the response is based on factual information.
• Legal Concerns: Ensuring content complies with applicable regulations before delivering the response. 

Key Ranking Factors in AI Search Environments

We did a study on AEO (answer engine optimization) visibility impact variables within AI searches on LLMs. Our research found that visibility in AI search isn’t just about one metric, but is shaped by a spectrum of signals. And while there is some overlap between SEO and AI SEO, LLMs generally use 15 factors to determine what and who to include in their responses. Some of them include:

• Content Signals: Factors like relevance, structure, and freshness that help AI systems understand and surface your content.
• Authority & Trustworthiness: How credible your brand appears through citations, rankings, and expertise.
• Engagement & Social Proof: Signals like reviews, sentiment, and social sharing that indicate content value to real users.
• Technical Performance: Infrastructure-related metrics like page speed and crawlability that affect discoverability.
• Consistency & Coverage: Regularly updated and comprehensive content, especially important in regulated or fast-moving sectors.

For an even deeper dive into these 15 variables impacting AI visibility, check out our AEO periodic table.

Checklist for Optimizing Content for LLM Visibility

AI-driven search is changing how content is surfaced. This checklist will help you optimize your pages for better visibility across platforms like ChatGPT, Perplexity, and Google’s AI Overview.

Content Quality & Clarity

• Does the content clearly answer specific questions your audience is likely to ask?
• Is your writing free of fluff and jargon, using natural, conversational language?
• Are complex ideas explained simply and logically, with examples or analogies?
• Have you formatted your content with short paragraphs, bullet points, and descriptive subheadings? 

Factual Accuracy & Trustworthiness

• Are all claims backed by credible sources, cited with links when possible?
• Are stats and references up-to-date (within the last 12-18 months)?
• Have you fact-checked common industry assumptions or figures?
• Is your content free of promotional exaggerations or vague generalizations? 

Structure & Semantic Clarity

• Is your content organized to follow a clear topical structure (H1 → H2 → H3)? 
• Have you included FAQs, definitions, or step-by-step processes where relevant?
• Are you using schema markup (FAQPage, HowTo, Article) to help AI interpret your content?
• Have you optimized for semantic relevance, using related terms and entities naturally? 

Technical Signals for AI Visibility

• Is your page indexable and crawlable (no blocking robots.txt or missing meta tags)?
• Are you using canonical tags correctly to avoid duplicate content confusion? 
• Have you optimized your page title and meta descriptions for clarity and intent? 
• Are your images described with alt text to support multimodal AI tools? 

LLM Discovery & Monitoring

• Have you tested how your content surfaces in AI tools like Perplexity, AIO, or ChatGPT? 
• Are you monitoring mentions, citations, or quotes from AI search platforms?
• Have you optimized for branded queries and long-tail questions that AI tools tend to prefer? 

Overall, think of your content as training data. If an AI system were learning how to explain your topic, would it choose you as the source?

How to Measure AI Search Visibility

Unlike traditional SEO, where you can track SERP positions, traffic, and click-through rates, visibility in AI search platforms is more opaque. But don’t worry, that doesn’t mean it’s immeasurable. Understanding whether your content appears in AI searches requires adopting some new measurement tactics.

Here are some ways to track your AI search footprint: 

AI Mentions & Citations 

Periodically prompt tools like ChatGPT, Gemini, and other platforms with relevant questions about the nature of your business. Then, see where your brand is mentioned, linked to, or paraphrased in these queries.

For instance, an athletic shoe brand might input queries like “what to look for in a running shoe” or “best trail running shoes” into various AI search engines. By monitoring which results they appear in and tracking this over time, they can continuously refine their content to maintain and improve visibility across AI search platforms.

Monitoring Brand Queries

LLMs often reference brands when talking about tools, comparisons, or recommendations. Track how your brand appears when users ask, “What's the best [tool/category]?” or “Alternatives to [competitor].” 

Some queries to search for include

• Alternatives to [your brand] for [your product’s purpose].
• How does [your brand] compare to [top competitor]?
• Is [your brand] a good choice for [your target audience]?

Watch for where your brand is mentioned, if the response gets your positioning correct, and whether your competitor’s benefits are being overemphasized. 

AI Inclusion Rate

Another key part of measuring your AI search performance is building internal benchmarks. Create a recurring internal process (monthly or quarterly) to compare how often your content surfaces in AI answers vs. your competitors. 

For example: 

Let’s say you’re competing with two other payroll software companies. In this case, you could: 

• Prompt Perplexity with “Best payroll platforms for startups” and log how often each brand is mentioned. 
• Repeat this across tools. 
• Track the percentage of prompts where your brand appears over time. 

Then build a dataset that shows: 

• “In Q1, our inclusion rate in AI answers was 20% across 20 key prompts.”
• “In Q2, we increased to 35% by improving our comparison pages and publishing original thought leadership content.” 

Use this to benchmark your visibility in the AI ecosystem, the same way you would with SERP rankings in traditional SEO. 

Use AI Visibility Tools

Platforms like Goodie allow you to analyze how and where your content appears in AI responses. Instead of manually inputting queries and tracking where and how you show up, AI visibility tools collect the data for you. 

This saves time, allowing you to analyze the data. Goodie also feeds back recommendations on what to implement to improve your visibility and sentiment within AI Search engines.

How to Influence LLM Training Data

If SEO is about ranking in search results, AI SEO is about being learned by search engines. LLMs are trained on a massive corpora of public data, so your goal is to get your content into the spaces AI is likely to consume. 

Some tactics for influencing what LLMs learn include: 

• Publish on high-authority, crawlable domains.
• Create original content at scale. Large models deprioritize duplicate or syndicated content, so unique viewpoints and in-depth articles help AI associate your brand with specific expertise. 
• Earn links and citations from reputable sources. The more you’re mentioned in content that’s crawled frequently, the more likely your brand is to become a part of AI’s “memory.” 
• Update your content frequently. Some LLMs (like Perplexity) incorporate live browsing. Fresh content isn’t just for users; it’s also for real-time indexing by AI search crawlers. 

It helps to think about your website like a textbook. The better organized, accurate, and cited your website content is, the more likely LLMs will “read” and “remember” it during training and retrieval. 

The Future of AI SEO: What’s Next?

AI SEO isn’t a passing trend – it’s a paradigm shift. As generative search becomes the default experience across browsers, voice assistants, and enterprise platforms, visibility will hinge less on how you rank and more on how you’re remembered. 

Here’s what to expect in the next wave:

• Searchless Discovery: LLMs embedded in operating systems, chatbots, and apps will answer user queries before they even have to search. 
• AI-First Content Formats: While content quality will always be important to users, search engines, and AI engines, it’s likely that in the future, content will be designed for machine-parsing first: so schema-rich, fact-based, highly structured assets. 
• Real-Time AI Analytics: Emerging AEO tools are already enabling marketers to track AI citations, prompt coverage, and model-specific visibility. The capabilities of these tools are already expanding. 

As search behavior continues to shift from searching to asking, staying relevant as a brand means rethinking not just how you optimize your content, but also teaching these machines to trust you. 

The Path Forward: Thriving in an AI-First Search Landscape

AI has fundamentally shifted how visibility, authority, and discovery work in the age of large language models. As search continues to evolve from static results to dynamic, AI-sourced answers, the brands that thrive are the ones adapting their strategies to serve both humans and machines. 

This means going beyond keywords to focus on clarity, trust, structure, and relevance. Think of your content not just as a web page to be ranked, but as a trusted source to be cited, paraphrased, and shared by AI. If traditional SEO has been about climbing ranks, AI SEO is about earning a spot within the answer.

Your best shot at staying visible is becoming the source AI trusts to explain your topic to the world.

AI SEO: Frequently Asked Questions

• What is the difference between retrieval-based and static AI models?
  
  • Static LLMs (like default ChatGPT) rely on fixed training data and don’t fetch new information. Retrieval-based models (like Perplexity or ChatGPT with browsing) pull real-time web content to answer questions, so they can surface newer content.
• What tools can help measure AI search visibility?
  
  • AI visibility tools like Goodie allow you to track where your content appears in AI responses, monitor citations, and analyze brand sentiment across AI search platforms. 
• Do I still need to care about traditional SEO if I’m optimizing for AI?
  
  • Yes! AI SEO builds on traditional SEO – it’s not about replacing it. While the focus shifts from “ranking” to “referencing,” foundational practices like crawlability, page speed, and high-quality content will always remain essential practices.