How to Conduct a Comprehensive AI Visibility Audit
How to Conduct a Comprehensive AI Visibility Audit
Establish a baseline for how Large Language Models (LLMs) perceive and recommend your brand to identify gaps in your AI Readiness Score.
What You'll Need
- Accounts for ChatGPT (OpenAI), Claude (Anthropic), and Perplexity AI
- A list of core brand keywords and primary product categories
- A spreadsheet for documenting response patterns and citations
Steps
Step 1: Define Baseline Queries
Develop a set of prompts that mirror how customers search for solutions in your niche. Include direct brand queries, category-based comparisons (e.g., 'Best tools for X'), and problem-solving prompts where your product is a viable solution.
Step 2: Test Across Diverse LLMs
Input your defined queries into ChatGPT, Claude, and Perplexity. It is critical to use multiple models because each relies on different training sets and retrieval-augmented generation (RAG) methods, leading to varied brand representations.
Step 3: Analyze Citation Sources
Identify which websites the AI cites when mentioning your brand or competitors. Note whether the AI is pulling from your official site, third-party review platforms, or outdated press releases to determine which public signals are driving the response.
Step 4: Evaluate Sentiment and Accuracy
Review the AI's summaries for factual correctness and brand tone. Document any 'hallucinations' or outdated information, such as old pricing or discontinued features, that could mislead potential customers.
Step 5: Benchmark Competitive Share of Voice
Compare the frequency and prominence of your brand against key competitors in category-level prompts. Note if the AI consistently recommends a competitor over you and analyze the specific attributes the AI associates with the winning brand.
Step 6: Assess Entity Recognition
Ask the AI to describe your company's core value proposition and target audience. This reveals if the model has a clear 'entity' understanding of your business or if it is confusing your brand with another similarly named entity.
Step 7: Quantify the AI Readiness Score
Aggregate your findings into a scoring system based on visibility, accuracy, and sentiment. Use these metrics to establish a starting AI Readiness Score, providing a quantitative benchmark for future Generative Engine Optimization (GEO) efforts.
Expert Tips
- Use 'Incognito' or fresh sessions to avoid personalized bias in AI responses.
- Focus on the 'why' behind a recommendation by asking the AI to explain its reasoning.
- Prioritize updating high-authority third-party sites, as LLMs often trust external validation more than self-reported brand data.
See also
- What Is Generative Engine Optimization (GEO)?
- What Is an AI Readiness Score?
- How AI Models Decide Which Brands to Recommend
- How to Improve Brand Visibility in LLM Responses