This lesson breaks down the structural framework of a professional AI Visibility Audit, moving from data retrieval to strategic optimisation for generative engines.
12 min read
Foundations
Visual diagram
A flow chart showing data moving from a website and third-party sources into an 'AI Model Processing' box, outputting to a 'Generative Response' with lines looping back to identify 'Citation Sources'.
Section 1 of 9
Introduction
Transitioning from traditional SEO auditing to AI Visibility auditing requires a shift in perspective. While traditional audits focus on crawlability, indexing, and blue-link rankings, an AI Visibility Audit (AVA) examines how Large Language Models (LLMs) and Generative Search Engines (GSEs) perceive, synthesise, and reference a brand's data. This lesson provides a step-by-step breakdown of the audit's anatomy, ensuring practitioners can provide clients with actionable, data-driven roadmaps for the age of Answer Engine Optimisation (AEO).
Introduction
Lesson Quiz
Pass at 70%.
1. What is the primary difference between a traditional SEO audit and an AI Visibility Audit?
2. Which Schema.org types are most critical for establishing a 'source of truth' for an AI model?
3. In the context of an audit, what does 'Share of Model' (SoM) measure?
4. What is 'Attribution Analysis' in an AI Visibility Audit?
5. Which robots.txt user-agent is Specifically used by OpenAI to crawl for GPT-4 training data?
6. Why is 'Information Density' important for AI visibility?
7. What is the purpose of a 'Solution-Based Query' during an audit?
8. In the LuxBoutique example, why was the Conde Nast mention problematic?
9. What role does Wikidata play in an AI Visibility Audit?
10. How often should an AI Visibility Audit ideally be performed for a dynamic brand?
