Getting Started with AEO
A beginner's guide to answer engine optimisation
What is AEO and why does it matter?
AEO stands for Answer Engine Optimisation — the practice of ensuring your brand and content are represented accurately and favourably in AI-generated answers. Traditional search engines return a list of links; answer engines return synthesised responses drawn from multiple sources. This fundamental shift means that brands can no longer rely solely on ranking in the top ten search results. If an AI engine omits your brand from its answer, you are invisible to a fast-growing segment of your audience.
The business impact is significant. Research shows that AI engines typically mention only three to five brands per response in competitive categories. If your competitors appear and you do not, you are losing consideration at the exact moment a potential customer is forming a shortlist. Early adopters of AEO are already seeing measurable improvements in brand visibility, citation rates, and recommendation frequency across AI platforms.
AEO is not a replacement for SEO — it is an expansion. The signals that AI engines use to determine which brands and sources to include in their answers overlap with SEO signals but also extend into new territory: structured data quality, content freshness, entity disambiguation, and the presence of machine-readable metadata like llms.txt.
Understanding the AI engine landscape
The AI answer engine ecosystem is diverse and growing rapidly. Each engine has distinct characteristics that affect how it discovers, processes, and presents content. ChatGPT relies heavily on its training data and increasingly uses web browsing for real-time queries. Perplexity is search-first, retrieving and citing sources for every response. Gemini integrates deeply with Google's knowledge graph. Claude prioritises nuance and long-form reasoning. Understanding these differences is essential because your AEO strategy must account for how each engine discovers and evaluates your content.
AI Overviews and AI Mode represent Google's integration of AI directly into traditional search results. These features pull from Google's existing index but apply AI synthesis, meaning your SEO-optimised content may appear in AI answers even without additional AEO effort — but only if it meets certain structural criteria. Copilot leverages Bing's index, making Bing optimisation more important than it has been in years. DeepSeek brings a different model architecture with its own content preferences.
The key takeaway is that a multi-engine approach is not optional. A brand that is visible on ChatGPT but invisible on Perplexity is missing a significant portion of its AI-first audience. AEO Platform monitors all major engines simultaneously, giving you a unified view of your visibility across the entire AI search landscape.
Your first AEO audit
The best way to begin your AEO journey is with a baseline audit. An AEO audit evaluates your current visibility across AI engines, identifies structural blockers preventing citations, and benchmarks you against competitors. Think of it as a health check for your AI presence.
The audit process starts with defining your query bank — the set of questions your target audience is likely to ask AI engines about your category, product type, or service area. These queries are then run across all major AI engines to capture how each one responds. The responses are analysed for brand mentions, competitor mentions, citation sources, sentiment, accuracy, and completeness. This baseline data tells you exactly where you stand.
From there, the audit identifies technical blockers — issues like missing structured data, AI crawler blocks in robots.txt, lack of llms.txt, or content formatting that makes extraction difficult. It also evaluates content gaps: queries where competitors are mentioned but you are not. The output is a prioritised action plan that tells you exactly what to fix first for maximum impact on your AI visibility.
Key metrics to track
AEO introduces a new set of metrics that are distinct from traditional SEO KPIs. The most important is Share of Model — the percentage of relevant queries for which an AI engine mentions your brand. This is the AI equivalent of Share of Voice and gives you a high-level view of your brand's presence in AI-generated answers. A brand with 25% Share of Model in its category is mentioned in one out of every four relevant AI responses.
Citation Rate measures how often AI engines cite your domain as a source. While Share of Model tracks brand mentions (which may come from training data), Citation Rate tracks active sourcing — when the engine actually links to or references your content. High Citation Rate indicates that AI engines view your content as authoritative. This metric is particularly important on Perplexity and AI Overviews, where citations drive referral traffic.
Other key metrics include sentiment analysis (how positively or negatively AI engines describe your brand), competitor visibility (which rivals appear alongside you), and response coverage (what percentage of your query bank returns answers that mention any relevant brand). Together, these metrics give you a comprehensive dashboard for understanding and improving your AI presence.
Quick wins for immediate impact
While a comprehensive AEO strategy takes time to build, there are several quick wins that can improve your AI visibility within days. First, ensure your robots.txt is not blocking AI crawlers. Many sites inadvertently block GPTBot, Google-Extended, or other AI-specific user agents, preventing these engines from accessing and indexing your content. A single robots.txt change can unlock visibility.
Second, create or update your llms.txt file. This machine-readable file provides AI engines with a structured summary of your brand, products, and key content — think of it as a README for language models. Sites with well-formed llms.txt consistently show higher citation rates. Third, add structured data (JSON-LD) to your key pages — product pages, comparison pages, FAQ pages, and pricing pages. Structured data helps AI engines extract accurate, specific information rather than relying on training data that may be outdated.
Finally, audit your top-performing content pages for answer-readiness. AI engines prefer content that leads with clear, direct answers rather than burying key information below introductions or marketing copy. Reformatting your highest-value pages to be answer-first can produce measurable visibility improvements within a single model update cycle.
Building your AEO roadmap
Once you have your baseline audit and initial quick wins in place, the next step is building a structured AEO roadmap. A good roadmap balances short-term tactical fixes with long-term strategic initiatives. The typical AEO roadmap spans three phases: Foundation (weeks 1-4), Optimisation (months 2-3), and Scale (months 4+).
In the Foundation phase, you address technical blockers, set up monitoring, and establish your query bank. This includes configuring AI crawler access, implementing llms.txt, adding structured data to priority pages, and running your first comprehensive multi-engine scan. In the Optimisation phase, you begin creating and restructuring content based on audit findings — filling content gaps, building comparison pages, improving answer-readiness, and targeting specific queries where competitors are visible but you are not.
The Scale phase focuses on expanding coverage, automating monitoring, and integrating AEO into your broader content and marketing workflows. This is where features like smart alerts, automated action plans, and team dashboards become essential. AEO Platform provides tooling for every phase of this roadmap, from initial scan to ongoing optimisation at scale.
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AI Brand Monitoring
Track how AI engines mention, describe, and recommend your brand across every major model.
Technical AEO Auditing
Identify technical blockers preventing AI engines from discovering and citing your content.
Share of Model Tracking
Measure your brand's share of AI-generated recommendations vs competitors.
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