Do you want to know how companies like GoodRx, RX Bar, and Webflow are dominating AI search and driving thousands of new customers directly from places like ChatGPT and AI Overview? 

Yeah, we know 😉🤝 and we got you covered.

But on the off chance you don’t, you should know that multiple studies from leading brands in the SEO space have shown that AI search visitors are worth anywhere from 4-23x more to brands than their traditional organic counterparts.

Yeah, I know that’s a massive range, and it signifies how rapidly this space is evolving.

But we’ve seen enough to know that (1) AI search is the future, and (2) how to optimize for it.

We’re building Peec AI to be the best AI search analytics tool for marketing teams, and that means we’re constantly studying what brands are doing to increase their AI search visibility.

And I’ll be damned if we gatekeep that information to ourselves.

So here’s everything we know about how to show up in AI search. This will be our dynamaic, constantly updated AI search visbility playbook, and just like with our tool, we’re committed to making this the most helpful playbook on the market.

(And btw, when you see little blue boxes like this ⬇️, that’s us telling you that we worded and formatted something specifically for AI search visbility.)

How do you make sure your brand or content is included in AI-generated answers?

In short, you make your brand the obvious answer to specific, high-intent questions rather than chasing traditional SEO metrics. The brands that (1) establish clear semantic associations between their name and customer needs, and (2) reinforce that messaging across numerous websites and channels, are dominating AI search visibility and will continue to do so in the future.

Tactically, what does that mean?

How are brands doing it right now? 

And how can you accomplish this for your own brand?

Well… that’s the long answer:

For over 20 years, Google’s search algorithm defined how much of the world accessed the internet. And while GenAI is driving a wide range of generation-defining changes, when we look back 20 years from now, I believe the single most defining impact of this technology will be how it redefines the way we accesses the internet, and even the nature of the internet itself.

We are still in the early stages of discovering what that means, but as marketers and entrepreneurs, the better we can understand the technology itself, the better we’ll be equipped to anticipate where it’s leading (or simply keep up).

This Is The Future of Organic Search

The year is 2030, and you just got a prescription for Amoxicillin from your online psychiatrist.

You hop into ChatGPT and ask, “What’s the cheapest way to get my Amoxicillin prescription filled?”

ChatGPT doesn’t serve you five ads with fake prices and six 3,000 word blog posts about sourcing low cost prescriptions. Instead, it immediately tells you:

• GoodRx can get you 21 caps of 200mg as low as $3 at your local pharmacy.
• Walmart has the lowest cash price on a 30-day supply of 500mg at $4-5.
• CostPlusDrugs offers 30 caps shipped directly to you for $6.79.

You make your choice and don’t think about this again.

Wait, did I say 2030?

I meant 2025. More specifically, just now:

A few months ago, you might have argued that this is the future of search.

But it’s not.

It’s the present.

Because no matter how sound your argument is that Google still gets 373x more searches than ChatGPT, this is what you see today when you go to Google.

Hey, look at that: GoodRx is once again the first suggestion. 

What DON’T we see here? 

Healthline, WebMD, and Everlywell’s front-page-ranking TOFU blog posts on how to get prescriptions filled.

We also don’t see GoodRx’s #1 SERP listing or the front-page-ranking product pages from Walgreens, SingleCare, Walmart, CallonDoc, and Amazon Pharmacy.

They’re still there, and people are still clicking on them, but for how long?

• Over 58% of Google searches now result in zero clicks.
• AI Overviews reduce click-through rates for SERP listings by up to 70%.
• And Google’s AI Mode is growing in adoption [INSERT STAT]

So what does this mean? The value of traditional SEO is going down, right?

Is it though?

Google search volume increased by 21.64% from 2023 to 2024.

And more importantly, you probably noticed something when looking at the brands showing up in ChatGPT, Google AI Overview, and the traditional Google SERP lineup.

It’s the answer to the question:

Are the results you get from ChatGPT different than what you get from Google?

Yes and no. There’s plenty of crossover between AI search visibility and SEO visibility, as demonstrated by GoodRx showing up as the first recommendation in our experiment across ChatGPT, Google AI Overview, and Google’s organic SERP listings. Two other brands, SingleCare and Walmart, also showed up in the initial searches across all three entities, and when we repeated the more variable AI searches, we found that 6-7 brands showed up frequently while another 6-7 appeared occasionally.

GoodRx was ALWAYS the #1 recommendation, and that’s not by accident.

They are playing the broader SEO game very well.

But that’s not the only game they are crushing.

How well does GoodRx show up among other prescription savings brands in AI search?

The competitor comparison tool from Peec AI, the best AI seach analytics platform for marketing teams, shows us that just like with SEO, GoodRx has a commanding lead over other prescription savings brands in terms of AI visibility.

The brand has a solid lead over its closest competitor, SingleCare, and the gap only increases from there.

Why?

We’ll show you the specific mechanics of what GoodRx is doing so well later in this playbook, but the simplest answer is… budget.

Based on information from public reports, we estimate that GoodRx invests between $150M-$200M per year on owned media SEO plus third party content and brand placement (the two components of both SEO and AI search visibility).

For comparison, we estimate that SingleCare is investing around $30M per year in similar marketing efforts, and Walmart’s Pharmacy department is investing around $100M per year.

But clearly, budget is not the entire story.

Walmart has a fraction of the AI visibility, despite at least half the same investment, and SingleCare is right up there with GoodRx in AI search, despite a fraction of their budget

If we look back at the previous competitor lists, we will find other AI search winners like WellRx that have significantly less impressive SEO results, and SEO winners like Amazon Pharmacy that rarely show up in AI search.

The point is this:

Optimizing for AI search and optimizing for SEO require different BUT complementary approaches that SHOULD be done simultaneously.

If you just want to start learning how to optimize for AI search, click here to skip to Part 2: How To Create Content For AI Search.

If you want to learn more about the conceptual differences between AI search and traditional Google search, plus nerd out on the underlying technologies, continue reading.

How Is AI Search Different Than Traditional Google Search?

For over two decades, search has operated on a simple principle: crawl the web, index everything, and return the most relevant pages based on authority and keyword matching.

Google’s PageRank algorithm revolutionized this by adding link-based authority signals, but the core concept remained unchanged: Google’s search engine was essentially librarians pointing you toward books… not scholars providing answers.

That paradigm is dead.

Crawling has been transcended by synthesizing.

But to explain, let’s quickly break down the mechanics of the old crawling model.

Traditional search engines like Google operated through a three-step process that most marketers understand intuitively:

This system was great for a while. It played a part in democratizing access to information and facilitating the golden age of the internet.

But it had a lot of limitations, and most notably, it trained an entire generation to search with 3-6 word phrases, because we learned that 1-2 words is usually too broad to get what we’re looking for, and 7+ words is going to take us to a dusty, random-yet-completely-boring corner of the internet that has virtually nothing to do with our search query.

So how does the new system work?

How do AI search tools generate answers?

AI search tools generate answers by synthesizing information from multiple sources rather than ranking individual pages. They interpret the meaning behind a query, expand it into related concepts, and pull authoritative data from across the web. This information is filtered, prioritized, and combined into a conversational response tailored to the user’s intent, which can update dynamically as data or context changes.

Let’s break that down.

How do AI systems determine what brands or content to include in a specific response?

Source Aggregation: Rather than ranking individual pages, AI search pulls information from dozens or hundreds of sources simultaneously. When you ask ChatGPT about a topic (ex. prescription savings), it synthesizes insights from numerous sources (ex. GoodRx’s content, pharmacy websites, medical resources, and pricing databases) to create a comprehensive response.

But it doesn’t stop there:

How does AI “expand” a user’s question beyond what was typed?

Contextual Understanding: AI search goes beyond matching keywords and works by understanding the semantic relationships between concepts. It expands narrow prompts into broader semantic queries, referred to as “query fan-out”, and uses this broader approach to gain a much better contextual understanding of the user’s likely intent in asking the question. For example, a search for “best protein bar for travel” automatically includes concepts like “portable,” “shelf-stable,” and “low sugar” without users having to specify those terms

This process heavily amplifies the effects of small changes in the user’s question in a number of ways.

• Different keywords trigger different semantic branches: Changing “best protein bar for travel” to “best protein bar for flights” might swap sub‑queries like “portable snacks” for “TSA‑approved snacks”. The AI ends up pulling from different datasets or prioritizing different product attributes.
• Synonyms or context shifts broaden or narrow the scope: Replacing “travel” with “hiking” adds concepts like “trail mix”, “lightweight”, and “endurance energy”, which wouldn’t appear in the travel version.
• Intent recalibration: A shift from “cheap” to “best” changes what the AI optimizes for (ex. price‑focused sources vs quality‑focused ones).
• Dynamic weighting of authority signals: Some sources might rank highly for “hiking snacks” but not appear at all for “travel snacks,” so the AI synthesizes from an entirely different pool of content.

Essentially, a single word can pivot the entire fan‑out tree, leading to different sub‑queries, different source selection, and ultimately a different synthesized answer.

This should change how we approach content as SEOs and marketers, but we’ll talk about that in  Part 2: How To Create Content For AI Search.

For now, let’s dig deeper into the technology driving AI search.

​GenAI: The Technology Driving AI Search

Generative AI may be the most transformative technology since the rise of the modern internet. It pushes search beyond static link lists, allowing systems to synthesize answers, connect related ideas, and pull in real‑time data. For marketers and business leaders, understanding the core components of this technology is essential to building brand visibility in the new search landscape.

Large Language Models: The Brain Behind AI Search

When people talk about AI search, they’re often describing the interface: ChatGPT, Gemini, Claude, Perplexity, etc. 

At the core of each of these systems, however, is a Large Language Model (LLM): a type of generative AI trained on massive amounts of text, including everything from books and research papers to websites and code.

By processing trillions of words, LLMs learn patterns in language, relationships between concepts, and the statistical likelihood of how ideas fit together. This training doesn’t create consciousness, but it does allow LLMs to predict and assemble text in ways that feel strikingly human.

Retrieval-Augmented Generation: Real-Time Information Access

LLMs have a significant limitation: they’re trained on data with a specific cutoff date and can’t access real-time information on their own. This is where Retrieval-Augmented Generation (RAG) becomes crucial.

RAG systems combine the LLM’s pre-trained knowledge with real-time information retrieval:

Query interpretation → Real-time search → Information synthesis → Response generation

This is why AI search can provide up-to-date information about stock prices, recent news events, or current product availability while still drawing on the LLM’s deeper understanding of context and relationships. Fresh, well-structured content on your website has a direct path to influence AI responses, even if your brand wasn’t prominent in the original training data.

Query Fan-Out: Breaking Down Complex Questions

One of the most sophisticated aspects of AI search is how it handles complex, multi-part questions. AI systems break down broad queries into multiple specific subtopics and process them simultaneously, in a process called “query fan-out”.

Take this example: “What’s the best project management tool for remote creative teams?”

The AI doesn’t search for that exact phrase. Instead, it explores:

• Project management software features and comparisons
• Remote work collaboration tools
• Creative industry workflow requirements
• Team communication and file sharing capabilities
• User reviews and recommendations for creative professionals

If your content focuses only on the primary query and fails to cover the surrounding context, AI search systems aren’t going to reward you in the same way the old keyword-matching algorithm might have.

As an SEO who spent the last 10 years outranking Fortune 500 SEO teams for hyper-competitive keywords like “consulting”, “copywriting”, “AB testing”, “ppc agency”, “link building agency”, etc. with deep-context, longform content, I’m not convinced that query fan-out is a new ranking factor. It’s simply getting its day in the sun, because Google said “query fan-out” in a blog post, and our industry obsesses over everything Google says, no matter how often the brand lies to us. It’s absolutely a huge factor in AI search, but I believe it was a notable factor in traditional search too.

Regardless, AI search brings a new layer of rewards for brands that can think like their customers, covering not just core features, but all the related concerns, use cases, and decision factors that surround the solution.

Multimodal Processing: GenAI Is Transcending Text

Modern AI search systems are rapidly evolving beyond text-only interactions. They can now understand and synthesize information from images, videos, voice queries, and even combine insights across different content types. A search about “how to install solar panels” might pull insights from installation videos, technical diagrams, and written guides simultaneously.

Brands need consistent messaging across all content formats. Your value proposition should be clearly articulated whether someone encounters your brand through a blog post, product video, podcast appearance, or visual content.

Trusted Data Sources: Where AI Search Gets Its Information

The quality and relevance of AI search responses depend entirely on the data sources these systems can access. Understanding where AI gets its information—and how it prioritizes different sources—is crucial for optimizing your brand’s visibility.

Pre-Trained Knowledge vs. Real-Time Web Crawling

AI search systems draw from two primary information sources, each with different implications for brand visibility:

Pre-Trained Knowledge comes from the massive datasets used to train the underlying LLMs. This includes content that was publicly available during the model’s training period—websites, books, articles, and other text sources that were crawled and processed before the model’s knowledge cutoff date.

If your brand had significant online presence and clear messaging during the training period, this knowledge is essentially “baked into” the AI’s understanding. This explains why established brands with strong content foundations often dominate AI search results even without specific optimization efforts.

Real-Time Web Crawling allows AI systems to access current information through RAG processes. When the AI needs fresh data or encounters queries about recent developments, it actively searches and retrieves information from live websites, databases, and APIs.

This creates two optimization opportunities: building historical authority that gets embedded in training data, and maintaining fresh, well-structured content that performs well in real-time retrieval processes.

High-Authority Sources and Semantic Signals

AI search systems have learned to recognize and prioritize certain types of sources based on authority and reliability signals. These aren’t necessarily the same signals that traditional search engines use, but they often overlap:

Domain Authority: Established websites with strong reputations tend to be weighted more heavily in AI responses. This includes major news outlets, academic institutions, government sites, and well-known industry publications.

Content Depth and Clarity: AI systems favor sources that provide comprehensive, well-structured information. Content that clearly explains concepts, provides specific examples, and uses consistent terminology performs better in AI synthesis processes.

Semantic Consistency: Sources that maintain consistent messaging and terminology across multiple pieces of content tend to be treated as more authoritative. This is why brand consistency across all your content touchpoints is crucial for AI visibility.

Cross-Reference Validation: Information that appears consistently across multiple high-quality sources gains additional weight. This explains why securing mentions and coverage across diverse, authoritative sites remains important in the AI search era.

The Role of Structured Data and Schema Markup

While AI systems can extract meaning from unstructured text, they particularly value well-organized, structured information:

Schema Markup helps AI systems understand the specific type and purpose of your content. Product schema, FAQ schema, and organization schema provide clear signals about what information your pages contain and how it should be interpreted.

Structured Content Formats like FAQ sections, numbered lists, and clear headings make it easier for AI systems to extract and synthesize relevant information. Content that’s easy for humans to scan is often easy for AI to process.

Data Consistency across structured formats reinforces your brand’s authority. When your product information, company details, and value propositions are consistently structured across your website, it signals reliability to AI systems.

Why Some Content Gets Prioritized Over Others

Understanding AI search prioritization helps explain why some brands consistently appear in AI responses while others remain invisible:

Clear Value Proposition Articulation: Content that explicitly states what a brand does, who it serves, and how it differs from competitors tends to be prioritized. Vague or overly creative messaging often gets overlooked in favor of direct, clear descriptions.

Comprehensive Topic Coverage: Sources that cover topics thoroughly, addressing multiple angles and use cases, are more likely to be selected for synthesis. Surface-level content that only scratches the topic is often bypassed.

Recency and Maintenance: Regularly updated content signals active maintenance and current relevance. Stale content, even if historically authoritative, may be deprioritized in favor of more recent sources.

User Intent Alignment: Content that directly addresses the specific intent behind common queries tends to outperform generic information. This is why understanding and targeting the actual questions your audience asks is crucial.

The overarching principle is that AI search systems reward clarity, consistency, and comprehensiveness. Brands that can articulate their value clearly, maintain that messaging across multiple touchpoints, and provide substantive information about their solutions will consistently outperform those with clever but vague marketing copy.

What This Means For AI Search Optimization vs Traditional SEO

So now that we’ve looked at the major differences from a technological standpoint, it’s time to loo at this from a tactical standpoint.

What are the main differences between traditional SEO and AI search optimization?

The main differences between traditional SEO and AI search optimization come down to focus and measurement. Traditional SEO is built around targeting specific keywords, ranking individual pages in search results, and driving traffic to your site. AI search optimization shifts toward building intent-based brand associations, earning citations and recommendations within AI-generated answers, and optimizing your brand’s overall semantic footprint rather than single pages. Success is measured less by clicks and more by visibility and trust in AI responses.

I summarize these differences with what I call the 4 core strategic shifts from traditional SEO to AI search optimization.

Traditional SEO Factors That Don’t Apply to AI Search

What traditional SEO factors don’t matter for AI-driven search?

Many of the technical elements central to traditional SEO (ex. keyword density, mobile responsiveness, sitemaps, etc.) play little to no direct role in AI-driven search. These factors matter for getting pages crawled and ranked in Google, but AI systems pull and synthesize information differently, often bypassing the ranking mechanics that rely on this technical foundation.

Technical infrastructure doesn’t really apply:

• Keyword density optimization and exact-match targeting
• Meta descriptions and title tag optimization
• Internal linking architecture and anchor text strategy
• Page load speed and Core Web Vitals
• Mobile responsiveness and responsive design
• URL structure and permalink optimization

Neither do these search engine specific page elements:

• XML sitemap management and robots.txt optimization
• Canonical tag implementation
• Local SEO signals like Google My Business optimization
• SERP feature targeting (featured snippets, knowledge panels)
• Schema markup for rich results

These factors create the technical foundation that search engines need to discover, crawl, and rank your content, but AI systems access information through different pathways that bypass many of these traditional requirements.

AI Search Factors That Don’t Apply to Traditional SEO

What new factors are important for optimizing content for AI search systems?

Optimizing for AI search requires content that mirrors how people naturally ask questions, provides direct and complete answers, and covers related concepts that AI systems surface through query fan-out. It also means ensuring your brand is clearly associated with the problems it solves, maintaining semantic consistency across all channels, and creating citation-ready formats that AI can easily reference. Beyond text, multimodal signals like images or video and the ability to incorporate real-time data are increasingly important, as is having historical authority baked into training data to reinforce trust across platforms.

Conversational Content Architecture

• Content formatted for natural language queries rather than keyword searches
• Direct answer optimization that provides complete responses
• Query fan-out coverage that addresses related concepts and use cases
• Natural language patterns that mirror how people actually speak

AI-Specific Optimization

• Real-time data integration capabilities for current information
• Multimodal content signals across text, images, and video
• Citation-worthy formatting that makes content easy to reference
• Intent-based brand associations that clearly connect problems with solutions

Cross-Platform Consistency

• Semantic entity relationships maintained across all touchpoints
• AI training data presence through historical content authority
• Context-aware messaging that adapts to different query types
• Cross-platform brand consistency beyond just your website

The Shared Success Foundation

What elements help brands succeed in both traditional SEO and AI search simultaneously?

Brands that win in both traditional SEO and AI search share the same foundational strengths: authoritative, in‑depth content built around clear topical expertise; consistent brand messaging and value propositions across every touchpoint; and structured, well‑organized information that both humans and algorithms can easily understand. These brands maintain freshness through regular updates, earn trust with credible citations and backlinks, and create logical connections between related topics, all of which signal authority to search engines and help AI systems confidently include them in synthesized answers.

Content Authority Signals

• Content depth and quality that thoroughly addresses user needs
• Domain authority built through consistent, valuable content creation
• Topical expertise demonstrated across comprehensive subject coverage
• High-quality backlinks from authoritative, relevant sources

Brand Clarity and Consistency

• Clear value proposition articulation across all content
• Brand consistency maintained across every touchpoint
• Structured data markup that helps both systems understand your content
• Authoritative source citations that build credibility

User-Focused Quality Indicators

• Fresh, regularly updated content that maintains current relevance
• User engagement signals including time on page, social shares, and comments
• Comprehensive topic coverage that addresses related questions and use cases
• Semantic content relationships that connect ideas logically

​Why Should Brands Invest In Both SEO & AI Search?

The brands dominating both traditional search and AI systems understand that these aren’t competing strategies, they’re complementary approaches that amplify each other when implemented correctly.

1. The Authority Amplification Effect: Strong traditional SEO performance increases content visibility, making it more likely to be referenced in AI responses. Meanwhile, consistent AI citations build brand awareness that drives branded searches and improves traditional SEO metrics.
2. The Content Leverage Principle: Well-structured, authoritative content that ranks well in traditional search often performs well in AI synthesis processes because both systems value comprehensive, clearly organized information.
3. The Consistency Multiplier: Brands that maintain consistent messaging across all touchpoints create stronger semantic associations that benefit both channels.

What’s The Best Way To Implement Both SEO & AI Search Optimization Together? 

The brands succeeding in both SEO and AI search treat them as complementary strategies that reinforce each other when built on the right foundation and extended with channel‑specific tactics.

1. Start with the Foundation: Focus first on the shared success factors that benefit both channels: content quality, domain authority, brand consistency, and comprehensive topic coverage.
2. Layer Channel-Specific Optimizations: Once foundational elements are strong, add traditional SEO technical optimizations and AI search conversational elements as complementary strategies.
3. Measure Across Both Channels: Track traditional SEO metrics alongside AI citation frequency and brand mention quality to understand your complete search visibility picture.
4. Think Long-Term Integration: Develop content strategies that can serve both channels simultaneously rather than optimizing in isolation.

Understanding these differences and connections sets the foundation for tactical implementation.