Welcome to the definitive resource on AI Search Optimization. As artificial intelligence rapidly transforms how search engines deliver information, understanding the difference between traditional SEO and AI-powered search optimization is critical for modern brands. In this guide, we’ll break down the key trends, actionable strategies, and real-world examples you need to succeed in the evolving landscape of AI-driven search. Whether you’re a business owner, marketer, or agency professional, this article will give you the frameworks and insights to maximize your brand’s visibility both in classic search and within AI-generated responses.
Introduction: Why AI Search Optimization is No Longer Just SEO
The world of search is changing. It’s no longer enough to focus exclusively on traditional SEO tactics like keywords, backlinks, and on-page optimization. As AI engines like ChatGPT, Gemini, and Perplexity become primary sources of answers for millions, your brand’s presence in these environments is now just as important—and often governed by different rules. This new discipline is often called AI Search Optimization (AEO) or AI SEO, but the acronyms matter less than the reality: the strategies that worked for Google’s classic rankings don’t always guarantee visibility in AI-generated answers.
Throughout this guide, we’ll uncover why traditional SEO is only part of the equation, which new factors drive AI search performance, and how you can strategically fill the gaps for your brand across the search landscape.
The Evolving Landscape: Traditional SEO vs. AI Search Optimization
Let’s start by recognizing the core difference:
- Traditional SEO focuses on optimizing your website to rank higher in classic search engines like Google, Bing, or Brave.
- AI Search Optimization (AEO) targets visibility within AI-generated responses, such as ChatGPT’s answers, Google AI mode, and other large language models (LLMs).
While there is some overlap, the data shows these are distinct arenas—excelling in one does not guarantee success in the other. As the transcript highlights, “just doing traditional SEO does not guarantee performance as far as your brand being mentioned in AI generated responses.”
Key Data Point: Visibility Gaps Across Platforms
Using a real-world analysis, multiple brands were tracked across both traditional search engines and LLMs. The findings:
- Some brands like Chesterfield Service performed well across both traditional search and AI platforms—a rare outlier.
- Other brands such as Sludgebusters ranked well in Google, Brave, and Bing but were “practically invisible” on LLMs.
- Conversely, a few brands showed up frequently in LLMs but not in classic search results.
This inconsistency proves that traditional SEO success does not always correlate with AI search visibility. There are, however, actionable levers to improve your performance across both.
Understanding Citations: The Building Blocks of AI Search Visibility
The backbone of AI search optimization is citations—mentions of your brand across the web that LLMs use to synthesize answers. These citations fall into two main categories:
- First-Party Citations: Direct references to your own website or controlled properties. Example: Chesterfield Service cited in ChatGPT as a local service provider.
- Third-Party Citations: Mentions on platforms you don’t own, such as directories or review sites. Example: Yelp, Angie, Better Business Bureau.
Based on an extraction of AI citations for the query “who is the best plumber in Chesterfield, Missouri,” the data reveals:
- 43% of citations were first-party—direct links to the business’s own website.
- 72% of third-party citations came from directories.
- 12% came from social media sources.
- Other sources accounted for a negligible percentage.
Key takeaway: To maximize AI search visibility, you must dominate both first-party and third-party citation sources, especially directories.
Actionable Framework: Audit and Fill Your AI Search Gaps
Every brand has untapped opportunities—places where they’re underrepresented in either classic search or AI-generated answers. Here’s how to systematically uncover and address those gaps:
- List All Platforms: Map your brand’s visibility across major search engines (Google, Bing, Brave) and LLMs (ChatGPT, Gemini, Perplexity).
- Identify Gaps: For each platform, note whether your brand appears as a citation or mention for your target keywords.
- Prioritize Opportunities: Focus on platforms where you’re missing but competitors are present.
- Take Action: Develop a plan to optimize your presence on high-impact citation sources for each platform.
“All of these are holes that need to be filled,” the transcript notes. This is especially valuable for agencies, who can deliver measurable improvements by strategically targeting the right platforms.
Step-by-Step Guide: Boosting Third-Party Citations
Since directories account for over 72% of third-party citations in AI responses, here’s a practical approach:
- Identify Top Cited Directories: Run your main keywords through all major LLMs. Track which directories are most frequently cited.
- Optimize Your Profiles: For each directory (e.g., Yelp, Angie, Better Business Bureau, Home Guide), ensure your business profile is fully built out, up to date, and features robust content.
- Drive Reviews: Encourage satisfied customers to leave reviews, as LLMs favor profiles with strong user engagement.
- Monitor and Iterate: Regularly audit your presence and adapt as new citation sources emerge in AI results.
Example: For local home services, Yelp was cited “basically on every single LLM that we ran.” If you’re not on these top platforms, “you’re greatly decreasing your odds of seeing any brand appearance or visibility.”
Optimizing First-Party Signals: The Power of Original Content
First-party content—your own website’s pages—remains a critical source of AI citations. The data shows up to 42% of AI citations come from businesses’ own sites, especially for local queries. Here’s how to maximize your first-party impact:
- Build Localized Commercial Pages: Create relevant, original service pages tailored to each location you serve.
- Support with Topical Content: Develop supporting assets such as cost/pricing guides, FAQs, and data-driven posts that reinforce your commercial pages.
- Leverage Unique Data: Publish original statistics and insights from your business. LLMs are “citation bait” for fresh, first-party data—especially if it’s never been seen before.
For example, a plumbing company could create dozens of cost guides (“How much does it cost to replace a toilet in Chesterfield, Missouri?”), each targeting a unique variant and intent. The more granular and data-rich your content, the more likely LLMs are to cite your pages directly.
On-Page SEO for LLMs: The Essentials
To ensure your pages are picked up by both search engines and LLMs:
- Include your target keyword in the URL, title, meta description, and first sentence.
- Use H2 headings and incorporate NLP-driven secondary keywords to cover all facets of your topic.
- Make your pages comprehensive—address all common questions and subtopics identified via NLP extraction.
“Every single page that we build… is going to hit on all these topics,” the transcript advises. This not only boosts classic rankings but also increases your chances of being cited by LLMs as a topical authority.
Case Studies: Real-World Successes and Key Learnings
Case Study 1: Chesterfield Service – The Outlier Success
Chesterfield Service stands out as an example of a brand performing well in both traditional search and AI platforms. They appeared in the top results across Google, Brave, Bing, and were also frequently cited by LLMs for local service queries. The key factors in their success included:
- Strong presence on all major third-party directories
- Comprehensive, localized commercial pages
- Active collection of first-party data and statistics
This case proves that while rare, it’s possible to excel across all search environments with a holistic, multi-platform strategy.
Case Study 2: Sludgebusters – The Visibility Gap
Sludgebusters, despite ranking well in Google, Brave, Bing, and appearing in Google’s local pack, was “practically invisible on these LLMs.” This demonstrates the risk of relying solely on traditional SEO and the need for proactive citation building within AI-referenced platforms.
Case Study 3: Rankability’s Blog – Filling the Topic Gaps
The Rankability team followed a structured process: publish comprehensive content, monitor performance in Google Search Console, and systematically create new pages to address topic gaps. For example, after seeing interest in “AI search rank trackers,” they built dedicated pages for each variant—such as “ChatGPT rank tracker” and “Perplexity rank tracker.”
This approach led to:
- Increased rankings for multiple long-tail queries
- Stronger topic authority
- Broader brand visibility in both classic and AI search
Case Study 4: Traditional SEO vs. AI Search – The EMD Example
The brand ranked #1 for the keyword “St. Louis SEO consultant” in Google for years using an exact match domain (EMD) strategy. However, when tested in AI environments:
- Google AI mode: Zero visibility (did not show up at all)
- ChatGPT: Not cited or mentioned
- Gemini: No appearance
- Perplexity: Only cited in one instance, but not as a brand mention
The absence of a Google Business Profile was a key barrier. The lesson: “Really good SEO… does not correlate with brand mentions or even citations here.” Optimizing for AI search requires a different approach.
Practical Tips: Building Your AI Search Optimization Playbook
1. Be Everywhere: Multi-Platform Presence
AI models synthesize information from across the web, not just your website. “Doing good marketing is probably the best thing you can do going forward. Just be everywhere.”
- Set up and optimize profiles on all major directories
- Engage on social platforms relevant to your industry
- Encourage third-party reviews and citations
2. Systematic Content Expansion
Adopt a methodology for identifying and filling content gaps:
- Use NLP tools to extract related topics and subthemes for your main keywords
- Create dedicated pages for high-potential variants (e.g., “17 common plumbing issues in Chesterfield, Missouri”)
- Monitor Search Console and LLM citations to spot new opportunities
3. Leverage First-Party Data and Statistics
Original statistics are “citation bait” for LLMs. Whenever possible, publish unique data drawn from your operations. For example, a plumbing company could share average repair costs based on their own records—unique insights LLMs will favor over generic content.
4. Optimize for Both Classic and AI Search
While the rules differ, many on-page best practices remain effective:
- Keyword in URL, title, meta, and first sentence
- Comprehensive coverage of the topic using NLP-driven keywords
- Authoritative, well-structured content
5. Track and Measure AI Visibility
Don’t guess—regularly check how your brand appears in AI-generated answers for target queries. Adjust your strategies based on observed gaps and new citation patterns.
Common Pitfalls and How to Avoid Them
Many brands fall into traps that limit their AI search performance:
- Over-reliance on Traditional SEO: Ranking #1 in Google does not guarantee mention in AI answers.
- Neglecting Third-Party Platforms: Not being present on top directories drastically reduces citation odds.
- Failure to Update Content: LLMs crave fresh, unique data. Stale content is less likely to be cited.
- Ignoring Local Signals: For local queries, Google Business Profile signals are critical for both classic and AI search.
AI Search Optimization: Your Step-by-Step Execution Plan
- Audit Your Current Visibility:
- Search your target queries in Google, Bing, ChatGPT, Gemini, and Perplexity.
- Record where your brand appears as a mention or citation.
- List Top Cited Platforms:
- Identify which directories and social platforms are most frequently cited for your queries.
- Build and Optimize Profiles:
- Claim or enhance your listings on these critical platforms.
- Encourage reviews and add rich, up-to-date information.
- Expand First-Party Content:
- Create new localized commercial and supporting pages.
- Add unique statistics and internal data wherever relevant.
- Regularly Monitor AI Results:
- Set a schedule to review your appearance in AI-generated answers using all major LLMs.
- Iterate your strategy based on new findings.
Conclusion: The Future of Search is Omnichannel – Are You Ready?
The era of AI-powered search is here—and it’s rewriting the rules of digital visibility. As we’ve shown, traditional SEO is still valuable, but it’s now just one piece of a much larger puzzle. Brands that master both first-party content development and third-party citation building will command presence across classic search engines and the new frontier of AI-driven answers.
Remember the key data:
- 43% of AI citations are first-party, directly controlled by your website.
- 72% of third-party citations come from directories.
- Success stories like Chesterfield Service and the Rankability blog demonstrate the power of filling topic gaps and building multi-platform authority.
- Case studies also show the risks of relying solely on traditional SEO—visibility in Google does not guarantee appearance in LLM answers.
Actionable steps for your brand:
- Audit your visibility across classic and AI search platforms
- Dominate the most-cited directories for your industry
- Publish unique, localized, and data-rich content
- Track, measure, and iterate your approach for continuous improvement
The brands that thrive in this new landscape will be those who embrace Search Everywhere Optimization—leveraging every platform, every citation, and every opportunity to be seen. Start today, and position yourself for sustained success in both the classic and AI-powered search worlds.