Master the sequential workflow for improving AI engine visibility using the SeenAndCited framework: from initial discovery through to iterative measurement and refinement.
15 min read
Foundations
Visual diagram
A circular process diagram showing the six phases of the SeenAndCited methodology with arrows indicating a continuous feedback loop and 'Data-Driven Insight' at the center.
Section 1 of 10
Introduction
The SeenAndCited methodology moves beyond theoretical understanding of AI engines to provide a structured, repeatable framework for practitioners. Unlike traditional SEO, which often focuses on keyword rankings and backlinks, AI Visibility Practitioner (AVP) work requires a focus on semantic relationships, conversational relevance, and citation accuracy across Large Language Models (LLMs). This lesson breaks down our six-stage proprietary workflow: Discover, Monitor, Analyse, Recommend, Execute, and Measure. By following this sequence, practitioners can ensure their clients’ brands are not only mentioned but accurately cited and recommended by engines like Perplexity, ChatGPT (Search), and Gemini.
Introduction
Lesson Quiz
Pass at 70%.
1. Which phase of the SeenAndCited methodology involves identifying the current 'LLM Footprint'?
2. What is 'Share of Model' (SoM) used for in the methodology?
3. In the Analyse phase, why is understanding RAG (Retrieval-Augmented Generation) important?
4. Which of these is a 'Technical' recommendation for improving AI visibility?
5. What does it mean that LLM responses are 'non-deterministic'?
6. How can you track referral traffic from AI engines in Google Search Console?
7. What should you do if an AI 'hallucinates' incorrect facts about your brand?
8. What is a 'Comparison Hub' in the context of the Execute phase?
9. Which of these is NOT one of the six stages in the SeenAndCited methodology?
10. During the Recommend phase, why target third-party industry directories?
