Last month, a client sent me a screenshot. They had searched for their own product category on ChatGPT. The AI recommended three competitors. Their brand was not mentioned.
They had spent £8,000 per month on traditional SEO for two years. Their Google rankings were solid. Their organic traffic was growing. But in the world of AI-powered answer engines, they were invisible.
This is the problem Answer Engine Optimisation solves.
If your customers are asking ChatGPT, Perplexity, or Google AI Mode for recommendations, and your brand does not appear in the answer, you are losing revenue to competitors who have adapted. This guide covers exactly how to fix that.
What Is Answer Engine Optimisation?
Answer Engine Optimisation (AEO) is the practise of structuring your content so AI-powered answer engines like Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, and Claude can extract, understand, and cite your brand in their responses. Unlike traditional SEO, which focuses on ranking in search results pages, AEO focuses on getting your content selected as a trusted source when AI systems generate answers.
The difference is fundamental. Traditional SEO asks: "How do I rank higher?" AEO asks: "How do I get cited?"
When someone searches on Google, they get a list of ten blue links. They click, browse, and decide. When someone asks ChatGPT or Perplexity the same question, they get one answer. The AI synthesises information from sources it trusts and presents a recommendation. If the AI does not trust your content, or cannot extract the information it needs, you simply do not appear.
This is not a future trend. Over 150 million people use ChatGPT weekly. Google AI Mode has surpassed 1 billion monthly users. Perplexity grew from 10 million to 100 million monthly users in 18 months. AI Overviews now appear on more than 30% of informational Google queries.
If you want to understand how this fits into the broader space, read my guide on what LLMO is and why it matters.
How Answer Engines Choose What to Cite
Every answer engine uses a similar set of signals to decide which sources to trust. Understanding these signals is the foundation of AEO.
Content Quality Signals
- Answer engines look for content that provides direct, factual answers. This means:
- Clear headings that match the question being asked
- 40-60 word answer blocks directly below each heading
- Factual claims backed by data or sources
- Content that demonstrates expertise, not just keyword coverage
Entity Authority
Answer engines build trust based on entity consistency. If your brand name, descriptions, and expertise claims are consistent across your website, industry publications, and structured data, the AI learns to associate your brand with your category.
If your messageing is inconsistent, or your entity signals are weak, the AI has no reason to recommend you over a competitor with clearer signals. This is why building AI visibility requires consistent entity authority across every platform.
Structured Data
Schema markup helps answer engines understand your content. FAQPage schema tells the AI that your content includes questions and answers. Article schema tells it that your content is a published article with an author and date. Organization schema tells it who you are and what you do.
Without structured data, the AI has to guess. With structured data, you are telling it exactly what it needs to know. See my SEO audit checklist for schema implementation details.
Brand Mentions and External Consensus
Answer engines cross-reference your brand information against external sources. If authoritative publications, industry databases, and trusted platforms mention your brand consistently, the AI is more likely to cite you.
If the only source of information about your brand is your own website, the AI has limited reason to trust it. Building mentions in authoritative publications is important for AEO.
Freshness and Relevance
Answer engines prefer recent content. If your competitor published a complete guide last month and yours is from two years ago, the AI will favour the newer content. Keeping your content updated is not optional for AEO.
How to Optimise for Google AI Overviews
Google AI Overviews appear as AI-generated summaries at the top of Google search results. They synthesise information from multiple sources and present a direct answer with citations.
What Triggers AI Overviews
- AI Overviews appear primarily on informational queries. They are triggered when Google determines that a conversational answer would be more helpful than a list of links. This includes:
- Definition queries ("What is X?")
- How-to queries ("How do I Y?")
- Comparison queries ("X vs Y")
- Multi-step queries ("How do I do X and Y?")
Answer-Block Formatting
- The most important AEO tactic for AI Overviews is answer-block formatting. This means:
- A clear H2 or H3 heading that matches the query
- A 40-60 word paragraph directly below that answers the question completely
- Supporting detail after the answer block
Schema Markup Requirements
- AI Overviews favour content with proper schema markup:
- FAQPage: For content with question-and-answer sections
- Article: For blog posts and guides
- HowTo: For step-by-step instructions
- Organization: For brand information
How to Track AI Overview Citations
- Monitor your AI Overview presence by:
- Checking Search Console for AI Overview impressions
- Searching for your target keywords and seeing if AI Overviews appear
- Tracking which pages get cited in AI Overviews
How to Optimise for Google AI Mode
Google AI Mode is a standalone conversational search interface powered by Gemini. Unlike AI Overviews, which appear within standard search results, AI Mode replaces traditional results entirely with a conversational interface.
What Makes AI Mode Different
- AI Mode supports:
- Conversational follow-ups with context retention
- Multimodal inputs (text, voice, images, files)
- Deep Search (automatic query decomposition)
- Personal intelligence (Gmail, Photos integration)
- Third-party app connections (Instacart, Canva, YouTube Music)
Deep Search and How It Works
Deep Search automatically breaks down complex questions into smaller subtopics and searches them simultaneously. This means your content needs to cover a topic completely to be cited by Deep Search.
If your content only covers one aspect of a topic, Deep Search is less likely to cite it. Pages that cover a topic from multiple angles, with data, examples, and expert analysis, are more valuable to Deep Search.
Conversational Content Structure
AI Mode is conversational. Your content should be too. Use question-form headings, provide complete answers, and structure your content as a logical progression of ideas.
Read my complete guide on Google AI Mode and how it works.
How to Optimise for ChatGPT
ChatGPT uses a combination of training data and live web search to generate answers. Understanding how ChatGPT selects sources is important for AEO. If you want to see how ChatGPT compares to other answer engines, read my ChatGPT vs Perplexity vs Gemini comparison.
How ChatGPT Selects Sources
- ChatGPT selects sources based on:
- Entity authority: Consistent brand information across the web
- Content depth: Complete coverage of a topic
- Factual accuracy: Verifiable claims with sources
- Recency: More recent content is preferred for current topics
Entity Authority and Consistency
ChatGPT builds its understanding of your brand from multiple sources. If your brand name, descriptions, and expertise claims are consistent across your website, social profiles, industry publications, and structured data, ChatGPT is more likely to recommend you.
If your messageing is inconsistent across platforms, ChatGPT has no clear signal about who you are or what you do.
Content Structure for Extraction
- ChatGPT extracts information from content that is structured clearly:
- Direct answers to specific questions
- Factual claims with sources
- Clear headings that match common queries
- Structured data that ChatGPT can parse
How to Track ChatGPT Citations
- Monitor your ChatGPT presence by:
- Asking ChatGPT directly about your category and brand
- Tracking which competitors ChatGPT recommends
- Monitoring referral traffic from ChatGPT
How to Optimise for Perplexity
Perplexity is an AI-powered answer engine that favours recent, authoritative content with clear citations. It is particularly popular among researchers and professionals.
What Perplexity Favours
- Perplexity selects sources based on:
- Recency: More recent content is strongly preferred
- Authority: Content from recognised sources and publications
- Citations: Content that includes verifiable data and statistics
- Depth: Complete coverage of a topic
Source Selection Criteria
- Perplexity tends to cite content that:
- Includes original research or unique data
- Provides specific numbers and statistics
- Is published on authoritative domains
- Is frequently updated
Content Format Preferences
- Perplexity favours content that is:
- Structured with clear headings
- Written in a direct, factual style
- Includes source citations
- Contains specific data points
How to Track Perplexity Citations
- Monitor your Perplexity presence by:
- Searching for your target keywords on Perplexity
- Checking which sources Perplexity cites
- Tracking referral traffic from Perplexity
How to Optimise for Claude
Claude is an AI assistant developed by Anthropic. It prioritises depth, accuracy, and factual sourcing.
What Claude Prioritises
- Claude selects sources based on:
- Depth: Complete, multi-angle coverage of a topic
- Accuracy: Factual claims that can be verified
- Context: Content that provides context and nuance, not just surface-level answers
- Sourcing: Claims backed by authoritative references
Content Depth Requirements
Claude favours content that goes deep. Surface-level listicles and thin content are less likely to be cited. Pages that provide expert analysis, original research, and complete coverage are more valuable.
Factual Sourcing
Claude is particularly sensitive to factual accuracy. Content that includes verifiable statistics, named sources, and specific data points is more likely to be cited than content with vague claims.
How to Track Claude Citations
- Monitor your Claude presence by:
- Asking Claude directly about your category and brand
- Tracking which competitors Claude recommends
- Monitoring referral traffic from Claude
Schema Markup for Answer Engines
Structured data is one of the most impactful AEO tactics. It helps answer engines understand your content without guessing.
Important Schema Types
FAQPage: Tells answer engines that your content includes questions and answers. This is the most directly useful schema for AEO because it explicitly marks Q&A content.
Article: Tells answer engines that your content is a published article with an author and date. This helps establish freshness and authority.
Organization: Tells answer engines who you are and what you do. Include your name, URL, logo, social profiles, and expertise areas.
HowTo: Tells answer engines that your content includes step-by-step instructions. Useful for how-to content that answer engines can extract.
How Structured Data Helps AI Systems
Structured data removes ambiguity. Without it, the AI has to parse your HTML and guess what each element means. With it, you are telling the AI exactly what your content is and how it should be interpreted. The key is to implement schema on every page, not just your homepage.
How to Measure AEO Success
AEO measurement is still evolving, but there are practical methods you can use today.
Tools and Methods for Tracking Citations
- Direct querying: Ask ChatGPT, Perplexity, Gemini, and Claude about your category. See if they recommend you.
- Search Console: Monitor AI Overview impressions and clicks.
- Referral traffic: Track traffic from AI platforms in your analytics.
- Automated monitoring: I built OpenCode SEO, an open-source platform that tracks AI visibility across certain platforms. It is not perfect yet, and it currently works with specific AI platforms only, but it gives you a starting point for automated citation monitoring.
- Brand monitoring: Track mentions of your brand across AI-generated responses.
Platform-by-Platform Monitoring
- Each platform has different monitoring requirements:
- Google AI Overviews: Search Console + manual checking
- Google AI Mode: Manual checking + referral traffic
- ChatGPT: Manual querying + referral traffic
- Perplexity: Manual checking + referral traffic
- Claude: Manual querying + referral traffic
KPIs for AI Search
- Track these metrics:
- Number of AI citations per month
- AI referral traffic
- AI Overview impressions and clicks
- Brand mention frequency in AI responses
- Competitor AI visibility comparison
Common Mistakes to Avoid
Writing for Keywords Instead of Questions
Answer engines answer questions. If your content targets keywords but does not answer the questions behind those keywords, it will not be cited.
Ignoring Structured Data
Structured data is not optional for AEO. Without it, answer engines have to guess what your content means. With it, you are telling them exactly what they need to know.
Not Monitoring AI Citations
You cannot improve what you do not measure. If you are not tracking your AI citation presence, you do not know if your AEO efforts are working.
Treating AI Search as "Future" Instead of "Now"
AI search is not coming. It is here. 150 million people use ChatGPT weekly. Google AI Mode has 1 billion monthly users. If you are waiting for AI search to "mature" before optimising for it, your competitors are already capturing the visibility you are missing.
What This Actually Means
Answer Engine Optimisation is not a replacement for traditional SEO. It is an additional layer. Your content still needs to be technically sound, fast-loading, and well-structured. But AEO adds specific requirements for answer-block formatting, entity authority, structured data, and citation monitoring.
The brands that start optimising for answer engines now will have a compounding advantage. The brands that wait will be playing catch-up for years.
If you want professional help implementing AEO across all platforms, my LLMO services cover exactly this. And if you want to understand how your brand performs across AI answer engines, book a free AI visibility assessment. Or if you are ready to hire an AI SEO consultant, see my guide on what to look for. I will audit your presence across Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, and Claude, and give you a clear picture of where you stand and what to fix.
Frequently Asked Questions
What is Answer Engine Optimisation (AEO)?
Answer Engine Optimisation is the practise of structuring your content so AI-powered answer engines like Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, and Claude can extract, understand, and cite your brand in their responses.
How is AEO different from traditional SEO?
Traditional SEO optimises for ranking positions in search results pages. AEO optimises for citation and recommendation within AI-generated answers. SEO targets clicks. AEO targets mentions.
How do I optimise for Google AI Overviews?
Create answer blocks of 40-60 words directly below each H2 heading. Implement FAQPage and Article schema markup. Build entity authority through consistent naming and structured data.
How do I optimise for ChatGPT?
Ensure brand information is consistent across all platforms. Create complete, well-structured content with clear headings and direct answers. Build mentions in authoritative publications.
How do I optimise for Perplexity?
Publish frequently updated content with verifiable data and statistics. Structure content with clear headings, direct answers, and source citations.
How do I measure AEO success?
Track AI citations by querying ChatGPT, Perplexity, Gemini, and Claude with relevant questions. Monitor Google AI Overview appearances using Search Console.
What is the difference between AEO, LLMO, and GEO?
AEO targets AI-powered answer engines broadly. LLMO focuses on large language models. GEO targets any AI system that generates responses. The tactics overlap substantially.
How much does AEO cost?
AEO consulting usually costs between £1,500 and £15,000 per month for UK-based consultants, and between $2,000 and $25,000 per month for US-based consultants.