What is AI Search Optimisation?
AI search optimisation is the practice of structuring your content, technical setup, and authority signals so that AI-powered search engines — ChatGPT, Perplexity, Google AI Overviews, Gemini — cite your brand when answering questions in your industry.
Why It Matters
Search is splitting. Google still dominates, but an increasing share of queries now go through AI-powered platforms — ChatGPT, Perplexity, Gemini, and Google's own AI Overviews. These systems do not return ten blue links. They generate answers and cite sources.
If your content is not structured for AI retrieval, you are invisible in this channel. Traditional SEO gets you ranking in Google's organic results. AI search optimisation gets you cited in the AI-generated answers that sit above, beside, or instead of those results.
This is the umbrella discipline. GEO, AEO, and LLMO are all subsets of AI search optimisation — each focusing on different aspects of the same goal: making your content the source that AI systems reference.
How It Works
AI search optimisation operates across three layers:
- Content structure — AI systems prefer content with clear definitions, specific data points, and logical heading hierarchies. Citable content — statements formatted so an LLM can extract and quote them — is the foundation.
- Technical accessibility — AI crawlers (GPTBot, ClaudeBot, PerplexityBot) need access to your site. If your robots.txt blocks them, they cannot index your content. Schema markup helps them understand what your content is about.
- Authority signals — LLMs assess source credibility through backlinks, brand mentions, author expertise, and citation frequency. Being referenced by other authoritative sources increases your share of model.
The three layers work together. Great content that AI cannot crawl is invisible. Accessible content without authority gets outranked. Authority without structured content gets summarised instead of cited.
Common Mistakes
Treating AI search optimisation as a separate project from regular SEO. It is not. Most of the tactics — structured content, schema markup, internal linking, E-E-A-T — benefit both traditional and AI search. The best approach is to optimise for both simultaneously.
The other mistake is optimising for one AI platform only. ChatGPT, Perplexity, and Google AI Overviews each retrieve content differently. A good AI search strategy makes content universally well-structured rather than gaming one specific system.
How I Use This
My AI search optimisation service covers all three layers — content restructuring, technical AI-crawler access, and authority building. The AI visibility assessment measures where you stand across ChatGPT, Perplexity, and Gemini before we start, so improvements are measurable.
Related Services
How BrightIQ uses AI Search Optimisation
This concept is central to the following services:
Related Terms
AI Visibility Assessment
An AI visibility assessment measures where and how often your brand appears in AI-powered search results — across ChatGPT, Perplexity, Google AI Overviews, and Gemini — and identifies what to fix so you show up more.
Answer Engine Optimisation
Answer engine optimisation (AEO) is the practice of formatting your content to directly answer specific questions, so that search engines and AI platforms use your site as the source in featured snippets, AI Overviews, and conversational search results.
Citable Content
Citable content is content structured so that AI systems and large language models can extract specific claims, definitions, or data points and reference them directly in generated answers — making your site the source they cite.
Generative Engine Optimisation
Generative engine optimisation (GEO) is the practice of structuring your website content so that AI-powered search engines — like ChatGPT, Perplexity, and Google AI Overviews — cite your brand when answering questions in your industry.
Large Language Model Optimisation
Large language model optimisation (LLMO) is the practice of making your content more likely to be retrieved, referenced, and cited by large language models like GPT-4, Claude, and Gemini when they generate answers to user queries.
Schema Markup
Schema markup is structured data code (typically JSON-LD) added to web pages that helps search engines understand the content — identifying entities like products, businesses, articles, and FAQs so Google can display rich results with star ratings, prices, and other enhanced features.
Share of Model
Share of model is a metric that measures how often a brand is mentioned or cited by AI language models compared to its competitors — the AI equivalent of share of voice in traditional marketing.