Intermediate
30-40 hoursAI Visibility Practitioner
A practical, intermediate-level certification programme that moves students from understanding AI visibility concepts to applying them in real-world engagements. Students learn to audit AI visibility, analyse authority and citations, benchmark competitors, prioritise actions, monitor changes and communicate measurable outcomes — fully aligned with the SeenAndCited methodology: Discover, Monitor, Analyse, Recommend, Execute, Measure.
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Module 1: Practitioner Foundations
Bridge from theory to practice. Establish the practitioner mindset, the SeenAndCited methodology and the working toolkit students will use throughout the course.
- Lesson 1From Concepts to PracticeThis lesson details the transition from theoretical AI knowledge to practical client delivery, outlining the responsibilities, workflows, and deliverables of an AI Visibility Practitioner.12 min
- Lesson 2The SeenAndCited Methodology in PracticeMaster the sequential workflow for improving AI engine visibility using the SeenAndCited framework: from initial discovery through to iterative measurement and refinement.15 min
- Lesson 3Setting Up Your Practitioner ToolkitThis lesson establishes the essential software, data sources and monitoring environments required to measure and influence AI visibility across major LLMs and search engines.12 min
- Lesson 4Scoping an AI Visibility EngagementMaster the art of scoping AI visibility projects by defining clear KPIs, technical boundaries, and tangible deliverables that align with commercial client goals.12 min
- Lesson 5Working With Brands, Sites and EntitiesMaster the methodology of mapping a subject across brand perception, technical site performance, and Knowledge Graph entity status to define its complete AI visibility footprint.12 min
Module 2: AI Visibility Auditing
Run a structured, repeatable AI visibility audit from kickoff to first findings.
- Lesson 1Anatomy of an AI Visibility AuditThis lesson breaks down the structural framework of a professional AI Visibility Audit, moving from data retrieval to strategic optimisation for generative engines.12 min
- Lesson 2Audit Inputs and Data CollectionMaster the methodology for identifying seed URLs, defining prompt libraries, and selecting competitors to create a robust data foundation for AI visibility audits.12 min
- Lesson 3Auditing Technical DiscoverabilityMaster the technical essentials of AI bot accessibility, focusing on robots.txt configurations, schema validation, and rendering efficiency for LLM crawlers.12 min
- Lesson 4Auditing Content for AI ConsumptionMaster the technical and semantic evaluation of content to ensure it is easily parsed, understood, and cited by Generative Engine Optimization (GEO) systems and LLMs.12 min
- Lesson 5Auditing Authority and Entity SignalsDevelop a systematic framework for evaluating brand authority and entity strength within the Knowledge Graph to improve AI-driven visibility and trust signals.15 min
- Lesson 6Producing the Audit ReportMaster the art of translating raw AI visibility data into a prioritised, client-ready report that connects technical AI engine performance to business impact and growth.12 min
Module 3: Discoverability Analysis
Diagnose whether AI systems can find, fetch and understand the content.
- Lesson 1How AI Crawlers Discover ContentMaster the technical pathways AI crawlers use to discover, fetch, and process content for Large Language Models and Generative Search engines.12 min
- Lesson 2Robots.txt for AI BotsMaster the strategic configuration of robots.txt for AI crawlers, balancing data protection with the necessity of being included in generative AI responses and LLM training sets.15 min
- Lesson 3Rendering and JavaScript PitfallsUnderstand how client-side rendering (CSR) and complex JavaScript frameworks hinder AI crawlers and LLM data fetchers from indexing and citing your core content.12 min
- Lesson 4Sitemaps, Feeds and llms.txtMaster the technical deployment of llms.txt, XML sitemaps, and RSS/Atom feeds to prioritise critical content and context for AI crawlers and Large Language Models.12 min
- Lesson 5Verifying AI Bot AccessMaster the technical methods for confirming AI crawler access through server log analysis, User-Agent verification, and real-time probe testing to ensure content reaches LLM training sets.12 min
Module 4: Authority Analysis
Measure and improve the off-site signals that make a brand trusted by AI.
- Lesson 1What Counts as Authority for AIExplore how AI models define and measure authority beyond domain ratings, focusing on mentions, reviews, structured data, and the concept of 'entity trust' in modern search.15 min
- Lesson 2Mapping Existing Authority SignalsLearn to audit and categorise a brand's authority footprint across search, social, and knowledge graphs to identify gaps in AI engine trust and attribution.12 min
- Lesson 3Identifying Authority GapsMaster the methodology for auditing brand authority across Large Language Models by identifying thematic and reputational gaps compared to market leaders.12 min
- Lesson 4Building Authority Acquisition PlansLearn how to architect data-led authority acquisition plans that target LLM training sets, high-clout citations, and partnership networks to close visibility gaps in AI-generated answers.12 min
- Lesson 5Validating Authority ImprovementsDevelop a systematic framework for tracking how AI engines perceive and integrate new authority signals, moving beyond traditional rankings to measure semantic relevance and attribution.12 min
Module 5: Entity Analysis
Strengthen the entity layer so AI knows who the brand is and what it does.
- Lesson 1Entities and Knowledge Graphs ExplainedMaster the fundamentals of entities and knowledge graphs to bridge the gap between traditional keyword-based SEO and semantic AI visibility.12 min
- Lesson 2Auditing Your Brand EntityMaster the audit of brand visibility across key entity repositories including Wikidata, Wikipedia, and Google’s Knowledge Graph to build a robust foundation for AI-driven discovery.12 min
- Lesson 3Schema.org for AI VisibilityMaster the specific Schema.org types and properties that enhance Large Language Model (LLM) comprehension and entity linking for AI-driven search results.12 min
- Lesson 4Entity Linking Across the WebMaster the techniques of linking disparate web mentions to a central entity node, ensuring Search Generative Experiences and LLMs recognise your brand as a single, authoritative authority.15 min
- Lesson 5Fixing Ambiguous and Duplicate EntitiesMaster the techniques for resolving entity clashes and ambiguity to ensure LLMs correctly associate your brand and subject matter with the right conceptual space.12 min
Module 6: Citation Analysis
Find where AI engines are already citing the brand — and where they should be.
- Lesson 1How AI Engines Choose CitationsMaster the specific signals used by ChatGPT, Perplexity, and Gemini to select and verify sources in a generative search environment.12 min
- Lesson 2Running Citation AuditsMaster a structured framework for identifying, categorising and auditing brand citations across major Large Language Models to benchmark visibility and site-source attribution.12 min
- Lesson 3Classifying Citation QualityMaster the art of auditing AI-generated citations by categorising them as branded, neutral, comparative, or negative to refine visibility strategies.12 min
- Lesson 4Citation Opportunity MappingMaster the process of identifying brand-relevant prompts where competitors are mentioned but your brand is absent, allowing for targeted visibility growth in AI responses.12 min
- Lesson 5Earning New CitationsMaster the strategic content shifts and authority-building tactics required to convert high-value brand mentions into verifiable citations within AI-driven search results.12 min
Module 7: Competitor Analysis
Benchmark visibility against the brands AI actually surfaces.
- Lesson 1Identifying AI CompetitorsLearn how to identify non-traditional competitors that AI models prioritise over your direct business rivals, focusing on informational parity and authority in RAG architectures.12 min
- Lesson 2Building a Competitor SetLearn how to define, categorise, and validate a primary competitor set for AI visibility tracking, distinguishing between traditional organic rivals and new algorithmic competitors.12 min
- Lesson 3Share of AI VoiceMaster the methodology for calculating Share of AI Voice (SOAV) to benchmark brand visibility against competitors within generative AI responses and LLM citations.12 min
- Lesson 4Competitor Strength and Weakness ProfilesDevelop the skills to audit competitor AI visibility across LLMs by mapping their content depth, technical provenance, and digital footprint strengths against your own performance.15 min
- Lesson 5Turning Competitor Insight Into ActionTransform raw AI visibility data into a strategic roadmap by identifying content gaps, refining brand sentiment, and optimising for specific LLM retrieval patterns.12 min
Module 8: Opportunity Identification
Find the highest-leverage moves a brand can make next.
- Lesson 1Sources of AI Visibility OpportunityMaster identifying AI visibility gains by analysing content relevance, authority signals, and entity relationships within the context of Generative Search and LLMs.12 min
- Lesson 2Scoring and Qualifying OpportunitiesMaster the art of prioritizing AI visibility tasks using the ICE framework to align organic growth strategies with client resources and commercial impact.12 min
- Lesson 3Prompt Gap AnalysisMaster the process of identifying 'Prompt Gaps' where competitors appear in AI engine responses but your brand is missing, and learn how to bridge those visibility deficits.12 min
- Lesson 4Content Gap Analysis for AIMaster the process of identifying specific information nodes and content formats that AI engines cite from competitors but missing from your own digital estate.12 min
- Lesson 5Building the Opportunity BacklogLearn how to establish a prioritised backlog of AI visibility opportunities using a structured scoring framework to ensure marketing resources focus on high-impact generative engine gains.12 min
Module 9: Monitoring Systems
Set up the ongoing monitoring that turns one-off audits into a programme.
- Lesson 1What to Monitor and WhyIdentify which AI visibility metrics require continuous automated tracking versus those best suited for periodic qualitative spot-checks to balance resource allocation effectively.12 min
- Lesson 2Prompt Tracking at ScaleDevelop a robust methodology for maintaining, versioning, and executing a core prompt set across multiple LLMs to track brand visibility consistently over time.12 min
- Lesson 3Alerting on Visibility ChangesDevelop a rigorous notification framework to detect AI citation shifts, model-specific ranking drops, and technical barriers preventing LLM indexing before they impact traffic.12 min
- Lesson 4Monitoring Bot ActivityMaster the technical identification of AI agents in server logs to predict visibility shifts before they appear in user-facing LLM results.12 min
- Lesson 5Building a Monitoring CadenceEstablish practical daily, weekly, and monthly workflows to track AI visibility without succumbing to data fatigue, ensuring actionable insights for client reporting and strategy.12 min
Module 10: Visibility Measurement
Quantify the impact of AI visibility work in numbers stakeholders trust.
- Lesson 1Core AI Visibility MetricsMaster the essential metrics for tracking performance on AI surfaces, focusing on Brand Mentions, Reference Share, Sentiment, and Citation Accuracy to quantify AI visibility.12 min
- Lesson 2Attribution for AI-Driven TrafficMaster the frameworks for identifying, segmenting, and reporting traffic originating from AI assistants and generative search engines using UTM parameters and server logs.12 min
- Lesson 3Measuring Citation LiftMaster the methodology for tracking how specific optimisations translate into increased brand and product mentions across major LLM interfaces and search engines.12 min
- Lesson 4Reporting Outcomes vs OutputsShift from tactical checklists to commercial impact by distinguishing between work completed and the resulting improvements in AI visibility and business revenue.12 min
- Lesson 5Building a Visibility DashboardMaster the art of synthesising AI-driven metrics and traditional SEO data into a cohesive dashboard that demonstrates tangible value to stakeholders and clients.12 min
Module 11: Strategy Development
Turn findings into a prioritised, time-boxed AI visibility strategy.
- Lesson 1From Audit to StrategyConvert AI visibility audit data into a prioritised roadmap. Learn to categorise findings, align with brand goals, and execute a tiered strategy for LLM dominance.12 min
- Lesson 2Prioritisation FrameworksMaster the application of ICE and RICE frameworks specifically for AI Visibility, prioritising high-impact optimisations that align with LLM training cycles and user intent.12 min
- Lesson 3Building a 90-Day Visibility PlanMaster the art of sequencing AI visibility optimisations into a high-impact, three-month roadmap with clear technical, creative, and analytical milestones.12 min
- Lesson 4Resourcing and RolesLearn how to map AI Visibility tasks to specific professional roles, bridging the gap between traditional SEO team structures and the requirements of generative engine optimisation.12 min
- Lesson 5Managing Risk and Trade-OffsLearn to navigate the complex balance between traditional SEO equity and emerging AI visibility needs while mitigating the risks of hallucinations and brand dilution.12 min
Module 12: Client Communication and Delivery
Communicate findings and progress in a way that wins and keeps client trust.
- Lesson 1Presenting an AI Visibility AuditMaster the art of translating complex LLM data into actionable business insights during client presentations to secure buy-in for AI visibility strategies.12 min
- Lesson 2Writing the Recommendations DocumentMaster the art of translating complex AI visibility data into a clear, prioritised roadmap that stakeholders and developers can execute without specialised AI knowledge.12 min
- Lesson 3Running Monthly ReviewsEstablish a consistent, data-driven monthly reporting cycle that demonstrates AI visibility growth, manages client expectations, and secures long-term buy-in for GEO strategies.12 min
- Lesson 4Quarterly Business Reviews for VisibilityMaster the art of translating technical AI visibility metrics into commercial outcomes during Quarterly Business Reviews to secure long-term client buy-in and budget.12 min
- Lesson 5Handling Difficult ConversationsMaster the art of navigating common AI visibility pitfalls, managing client expectations during algorithm volatility, and justifying scope changes for complex GEO projects.12 min
Certificate of completion
Complete all lessons to earn a SeenAndCited Academy certificate.
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