How To Optimise For Generative AI Search

This is something that, as a specialist SEO agency, we are asked regularly. Over the last year, we have seen significant changes in AI models, and as a result, companies want to get ahead of the curve to ensure their business appears in AI results. 

If your brand isn’t appearing in the AI Overview in Google or coming up in LLM (Large Language Model) results, you are effectively missing out on a massive portion of the modern customer journey.

So What Is GEO, And Why Should You Care?

Traditional SEO methods are about hitting the right keywords and optimising your click-through-rate (CTR), but GEO (Generative Engine Optimisation) is a completely different beast. It is less about individual rankings and more about your website’s digital influence and how efficiently AI models can extract and relay your information.

Unlike the traditional results of Google or Bing, generative search collates information from across the web to provide the user with a single, streamlined response. To succeed in GEO, you must go a step further than simple relevancy; you must establish undeniable authority in your niche so that AI models view your data as the most reliable source to quote.

The three key signals AI Models look for are

To rank for AI search results, your content must meet the following criteria:

• Directness: How quickly do you answer the user’s prompt? AI models prefer content that gets to the point immediately, often looking for “answer targets” within the first 200 words of a page.

• Authoritiveness: Does your website follow Google’s E-E-A-T Guidelines? AI Models prefer citing experts and unique perspectives, rather than recycled general content.

• Extractability: Is your on-page content structured in a way that an AI crawler can “digest” it easily? Complex code, unoptimised scripts, or technical jargon can harm your searchability.

3 Ways To Build a Strong AI Search Strategy

1. Focus on Information Accessibility
   AI models are trained on what already exists across the internet. If your blog posts simply state the same thing as the Top 10 results in traditional search engines such as Google, the AI model has no reason to cite you. 

   We advise our clients to combat this by focusing on providing first-hand experience. This means publishing original case studies, proprietary data, and real-life project outcomes. When you offer a unique perspective or a fresh data point that AI crawlers cannot find anywhere else, you become the primary source. This makes it highly likely that an LLM will credit your brand when a user asks a relevant question. 

2. Optimise Your Content for Conversational Intent
   What we mean by this is, people aren’t just searching for “SEO Agency Nottingham” anymore, they also ask AI models questions like “Who is the best SEO agency for my B2B business?” 

   In order to capture this traffic, your content must mirror this natural language. We recommend using Question-Header structures that directly mimic the questions your customers/audience is asking. By aligning your prose with the way people speak, you make it significantly easier for AI crawlers to map your content to specific user intents. 

3. Technical “AI-Ready” Requirements
   One of the most overlooked aspects in this new age of GEO is the focus shift from keywords. Now, this doesn’t mean you shouldn’t be targeting these keywords, but AI models understand the world as a graph of related topics such as: People, places, things & brands. 

To nail your technical setup to hit these signals, you must implement advanced Schema markup, such as FAQ, Service, or Organisation schema. This is a key strategic move we recommend at Repeat is placing this JSON-LD code at the very top of your HTML. This acts as an immediate translation for AI models and an early signal indicating what your pages are all about, who you are, and why you’re an authority. This structured data helps both traditional search engines and AI search engines cite your brand with a stronger chance of being referred to in search results.

How to Manage AI Crawlers On Your Site

One of the emerging trends that gained momentum in 2025 and is still evolving as we move deeper into 2026 is the birth of the LLMS.txt file. Much like the traditional Robots.txt file that tells search engines which pages to index, the LLMS.txt file is a new standard designed specifically for AI Search Engines. 

This is now a growing list of AI crawlers, such as GPTBot (OpenAI), that traverse the web with the sole purpose of feeding generative AI models with information. Part of a robust GEO strategy involves managing these crawlers as effectively as possible. 

By implementing an LLMS.txt file, you can provide a high-level, markdown-based summary of your website’s most important content, which allows you to:

• Direct AI Focus: Instruct crawlers to prioritise your most valuable and data-rich pages whilst ignoring the administrative pages that could potentially harm your authority score. 
• Control Information Flow: Ensure that AI models are synthesising your most up-to-date and accurate information, rather than legacy data. 
• Improve Extractability: Since the LLMS.txt is designed to be human-readable and machine-efficient, it acts as a fast track for AI models to understand your site’s core purpose. 

One of the things we believe at Repeat is that when it comes to generative engine optimisation, managing your website’s AI permissions is just as important as managing your keywords and uniqueness. It ensures that models like ChatGPT or Gemini look at your brand, and it sees exactly what you want it to see.

How We Audit for AI Citations

Our approach at Repeat for GEO is all centered around what we call the “Information Gap”. When we audit a client’s site for generative search, we don’t simply just look at their rankings (but they’re still not to be ignored!), we look at the specific data points that AI models are specifically looking for within your niche. 

For example, if every competitor is providing the same generic advice, the AI model has no reason to pick one over the other. So our methodology involves identifying that “Information Gap” that none of your competitors are currently filling. Specific details such as regional nuances, proprietary statistics, or a more unique perspective on a topic are crucial for AI models to cite our clients as the definitive source for that new information.