Marketers are at a crossroad.

The rise of AI search engines like ChatGPT, Gemini, Claude, and Perplexity is fundamentally reshaping how consumers discover and evaluate products. What once flowed directly to Google is now channeling into AI chat windows, creating the most significant shift in digital marketing since the dawn of search engines.

While most businesses panic about declining search traffic, smart marketers see an unprecedented opportunity.

Enter: Generative Engine Optimization (GEO) — the strategic discipline of ensuring LLMs mention, cite, and recommend your brand when users ask questions in your domain.

What is Generative Engine Optimisation?

GEO is the practice of making sure AI tools like ChatGPT and Gemini mention, cite, and recommend your brand — the way we once fought to rank on Google.

The goal of GEO is simple:

Ensure your brand is recommended by LLMs like ChatGPT and Gemini when users ask questions related to your domain.

Unlike traditional search marketing, GEO operates without ads, bidding wars, or complex backlink strategies. Success hinges on three fundamental pillars: relevance, authority, and trust — all filtered through the lens of artificial intelligence.

Why GEO Matters More Than Ever

The scary (and exciting) part?

There’s currently no official guidance from OpenAI, Google, or Anthropic on how to “rank” inside LLMs. There’s no equivalent of PageRank, no Search Console for AI models, and no set of ranking factors. And if we take these platforms at their word, it’s unlikely such tools will ever exist. Their promise is personalisation — that responses are dynamically generated using signals from across the internet. Offering a clear, public method to influence those outputs would contradict that core principle.

Marketing is on a free fall.

But smart marketers have always thrived in ambiguity.
They fall back on a reliable playbook one that predates Google itself.

The most successful teams are using a 3-step framework that’s been around since the beginning of modern digital marketing.

Step 1: Analyze Trends and AI Behavior Patterns

Before you start optimizing, you need to observe.

In traditional SEO, this meant analyzing SERPs, keyword volumes, and backlink profiles. In GEO, it means systematically analyzing how LLMs respond to queries in your space.

You want to understand:

• Source Preferences: What types of websites and content formats do LLMs consistently cite?

• Summarization Patterns: How do AI models structure responses for product categories in your industry?

• Brand Visibility: Which competitors consistently appear in AI-generated answers?

• Content Characteristics: How do tone, structure, and format influence citation likelihood?

Real-world example: If ChatGPT consistently mentions a specific CRM platform in sales-related queries, investigate the underlying content strategy. Is their success driven by comprehensive blog content, detailed case studies, or data-rich research reports?

This analysis phase focuses on reverse-engineering AI behavior patterns through systematic observation rather than code analysis.

Generative search optimisation softwares like Radix provide comprehensive monitoring tools to track LLM behavior across multiple platforms, making this analysis systematic and scalable.

Step 2: Form Data-Driven Hypotheses

Once you've identified patterns in LLM behavior, translate these observations into testable hypotheses.

This is where great marketers shine.

In GEO, hypotheses might sound like:

• LLMs prefer FAQ-style content because it provides structured, easily digestible information.

• Content with clearly attributed authors and credible sources receives higher citation rates.

• Regional product mentions vary significantly based on local press coverage and market presence.

• LLMs tend to reference comparison articles and listicles when answering product evaluation queries.

These hypotheses become the foundation for your content experimentation strategy.

Instead of producing generic blog content, you begin testing specific content formats, restructuring existing materials, and optimizing key landing pages based on AI behavior patterns.

Step 3: Execute Structured Experiments

With hypotheses in hand, the next step is to test them through structured, measurable experiments. Leading teams are running controlled variations of landing pages, adjusting tone, formatting, and citation styles to see what LLMs prefer.

Here’s what a few teams are doing:

Content Format Testing:

• Launching multiple variants of landing pages to test structural elements

• Embedding source-style phrasing and citation formats to boost credibility

• Creating comprehensive content clusters around high-intent prompts

• Refreshing existing SEO content with LLM-optimized introductions and conclusions

Authority Building:

• Developing thought leadership content that demonstrates deep industry expertise

• Creating comprehensive resource hubs that serve as definitive industry references

• Publishing original research and data that LLMs can cite as primary sources

• Building strategic partnerships with authoritative industry publications

Measurement and Optimisation:

• Tracking brand mention frequency across different LLM platforms

• Monitoring citation context and sentiment in AI-generated responses

• Analyzing correlation between content characteristics and citation rates

• Measuring user engagement and conversion from AI-referred traffic

Advanced GEO platforms like Radix AI enable marketers to track these metrics systematically, providing visibility into AI agent visits, LLM-referred traffic, and historical performance data.

The Competitive Advantage: Why Early Movers Win?

The companies investing in GEO experimentation today are establishing significant competitive advantages before the market matures and best practices become widely adopted.

Early data from GEO pioneers suggests that brands successfully cited by LLMs experience:

• Higher brand recognition and trust among target audiences

• Increased qualified traffic from AI-powered search interactions

• Enhanced authority positioning within their industries

• Improved conversion rates from AI-referred prospects

Take Action: Start Your GEO Journey with Radix

Successfully implementing GEO requires more than understanding principles—it demands the right tools and data insights. Radix is the most comprehensive platform built specifically for Generative Engine Analytics, providing the analytics and experimentation framework necessary to audit your current position, form data-driven hypotheses, and track the performance of your GEO experiments across all major LLM platforms.

You can start today with to evaluate how your brand gets recommended by ChatGPT, Perplexity and Google overviews.

Want to know how ChatGPT talks about your brand? Start with Radix's free trial — Analyse how LLMs cite, compare, and recommend you across AI search platforms like ChatGPT, Perplexity, and Google.