AI Search Optimisation (AEO)
The practice of structuring brand information, content and authority signals to increase citation probability in AI-generated answers across platforms like ChatGPT, Claude, Perplexity and Google AI Overviews.
Also known as: Generative Engine Optimisation, LLM Optimisation
AI search optimisation emerged as a distinct discipline between 2023-2025, after more and more people started using ChatGPT, Claude, and other AI systems.
Initially, it was treated as an extension of traditional SEO, but by 2024, it became clear that AI systems evaluated sources using fundamentally different criteria because they prioritised extractability and cross-source validation over link-based authority alone. A key difference.
The field now covers technical optimisation (structured data, AI crawler permissions), content strategy (citation-worthy formatting) and ecosystem authority (third-party validation across platforms).
As you know, traditional search engines return a list of ranked pages. Users click, read and decide. Simple and straightforward. AI search engines and assistants, on the other hand, generate direct answers by synthesising information from multiple sources. The AI reads on behalf of the user, selects trusted sources, extracts relevant information and presents a synthesised response.
So, the question went from "Will this page rank?" to "Will this source be trusted and cited?". Three core differences drive this change:
Selection happens before visibility, so your brand is chosen before your audience get to even see it.
AI search cross-references sources, decides which brands are trustworthy and then presents the synthesised information (and its choices) to the user.Authority is cross-referenced in real-time
Traditional search relies heavily on historical authority signals like backlinks. AI search cross-references claims against multiple sources during answer generation, evaluating consistency and validation across the web.Content must be easily extractable and without any risk
AI search prioritises content that can be safely extracted, reused and attributed without introducing factual errors or misrepresentation.
Let's go over the five dimensions that AI systems use to evaluate potential sources when generating answers:
Entity clarity: Can the system definitively classify what your brand is, what it does and what category it operates in? Ambiguous positioning reduces citation probability. AI systems build entity graphs connecting brands to categories, services, locations and relationships. Unclear categorisation or inconsistent self-description creates classification uncertainty, which triggers risk avoidance behaviour.
Authority reinforcement: Do independent third-party sources confirm the brand's expertise? AI systems cross-reference claims against external validation. Unlike traditional SEO where authority flows primarily through backlinks, AI authority comes from being mentioned, described and validated across multiple trusted sources: press coverage, industry directories, review platforms and editorial mentions all contribute to authority reinforcement.
Content extractability: Is the content structured in ways that allow safe reuse? Clear headings, definitions and factual statements increase extractability. AI systems prefer content that can be extracted without ambiguity. This means clear topic sentences, explicit definitions, structured lists and direct answers to common questions. Narrative or promotional framing reduces extractability.
Cross-source consistency: Do multiple trusted sources say similar things about the brand? Inconsistency signals risk and triggers hedging behaviour. When AI systems find conflicting information across sources, they either exclude the brand from answers or add hedging language ("some sources suggest..."). Consistency across owned properties, third-party mentions, and structured data reduces this risk.
Query relevance: Does the content directly answer the specific question being asked, or does it require inference? Direct answers get prioritised. AI systems match content to query intent with high precision. Content that directly addresses common questions in the field has higher citation probability than content requiring interpretation or inference.
→ Learn more: AI Search Ranking Factors
AI search optimisation and traditional SEO address different stages of the visibility journey. They complement rather than replace each other.
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Misconception 1: AI search optimisation is just SEO with different keywords
No, and here's why: AI systems evaluate entity-level authority and content extractability, not just keyword relevance. A page can rank well in traditional search but never be cited by AI systems if it lacks clear entity signals or extractable structure.
Misconception 2: More content improves AI visibility
Content volume matters less than clarity, structure and cross-source validation. A single well-structured page with strong external validation outperforms dozens of ambiguous pages. We've seen it first-hand with our website as well as clients' websites.
Misconception 3: AI search will replace traditional search
Again, no because both systems coexist and serve different user needs. Traditional search serves users who want to explore options. AI search serves users who want direct answers.
Misconception 4: You can optimise for AI search by prompting AI systems
AI systems draw from their training data and real-time web retrieval, not from individual user prompts. Optimisation happens at the source level (your content, your external mentions) not at the query level.
Misconception 5: AI search optimisation is only for large brands
AI systems evaluate source quality and relevance, not brand size. Smaller brands with clear positioning and strong external validation can achieve higher citation rates than larger brands with ambiguous messaging.
Unlike traditional SEO metrics (rankings, traffic), AI search optimisation tracks:
Citation rate: Frequency of brand mentions in AI-generated answers across tracked queries. Measured as percentage of relevant queries where the brand appears in the response.
Source attribution: Whether the brand is cited as a primary source with attribution (link, mention, or reference) versus being synthesised without credit.
Answer inclusion: Presence in AI Overviews, ChatGPT responses, Perplexity citations, Claude answers and similar AI-generated results.
Entity recognition: Accuracy of brand classification across platforms. Does the AI system correctly identify what the brand does, what category it operates in, and what problems it solves?
Competitive visibility: Share of voice versus category competitors. In queries where competitors are mentioned, how often does your brand also appear?
Platform coverage: Distribution of citations across different AI platforms. Some brands achieve high visibility on ChatGPT but low visibility on Perplexity, indicating platform-specific optimisation gaps.
These metrics reflect whether AI systems trust and use brand information, not just whether users click through. The goal is to become a default source AI systems reference when answering questions in your domain.
Structured data beyond Schema.org
While traditional SEO uses structured data primarily for rich snippets, AI search optimisation uses it for entity disambiguation. Organization schema, brand properties and relationship definitions help AI systems classify and connect entities accurately.
AI crawler management
AI platforms deploy specific crawlers (GPTBot, ClaudeBot, PerplexityBot) that require explicit permission in robots.txt. Blocking these crawlers eliminates AI visibility entirely.
llms.txt implementation
An emerging standard that provides AI systems with explicit guidance on how to understand and use site content. Functions as a machine-readable site guide optimised for LLM consumption.
Content extractability signals
Clear heading hierarchy, definition lists, structured Q&A formats and explicit topic sentences that AI systems can extract without ambiguity.
Cross-platform consistency
Ensuring brand information (name, description, category, location) remains consistent across owned properties, structured data, social profiles and third-party mentions.
Here's where AI search optimisation delivers the highest impact:
High-intent service categories
When users ask AI systems for service recommendations ("best AI search agency", "who offers technical SEO"), being cited directly captures demand without requiring click-through.
Complex explanation queries
When users need concepts explained ("what is AI search optimisation", "how does entity recognition work"), brands that provide clear, neutral explanations become trusted sources.
Comparison and evaluation queries
When users ask AI to compare options or evaluate approaches, brands with clear positioning and external validation appear in comparison frameworks.
Local and geographic queries
When users add location qualifiers ("AI search agency in London"), structured location data and local validation signals determine inclusion.
Emerging category definition
When a new category or practice emerges, brands that establish definitional authority early become the default sources AI systems reference.
Different content types serve different roles in AI search optimisation:
Definitional content: Neutral explanations of concepts, practices or categories have the highest citation probability for informational queries. Must maintain objectivity to be trusted as reference material.
Methodological content: Explanations of how processes work, what frameworks exist, or what approaches are valid. Cited when users ask "how to" questions or seek strategic guidance.
Comparative content: Neutral comparisons between approaches, tools or strategies are used in queries when users ask AI to evaluate options or explain differences.
Case evidence: Specific examples, outcomes or implementations. Cited when users ask for proof points or real-world applications.
Commercial content: Service descriptions, pricing, provider information. Cited only when users explicitly ask for providers or vendors. Lowest citation probability for general informational queries.
