Measuring a brand's visibility on conversational AIs is far from obvious. No Google Search Console for ChatGPT. No standardized metrics. No consensus on what to look at.
This absence of an established framework is both a problem and an opportunity. A problem because you need to build your methodology from scratch. An opportunity because actors who master AI visibility auditing today have a rare competitive advantage.
This guide details the methodology we developed after hundreds of audits. The steps, the indicators, the pitfalls to avoid, and the tools to automate what can be automated.
Why audit AI visibility
Before discussing methodology, let's clarify the exercise's utility.
Establish a baseline. Without an initial measurement, it's impossible to quantify the impact of your optimization efforts. The audit constitutes the T0 from which you'll evaluate progress.
Identify gaps with competition. A competitor is systematically cited when asked "best [your industry]"? That's strategic information. The audit reveals who occupies the space you covet.
Prioritize actions. You're visible on Perplexity but absent from ChatGPT? The audit directs efforts toward deficit platforms.
Demonstrate value. For agencies and consultants, the audit is a sales and proof tool. Showing a prospect their (non-)visibility creates immediate awareness.
Indicators to track
Four metric families structure a complete AI visibility audit.
Mention rate
The mention rate measures how often your brand appears in AI responses for a set of target queries. It's the most direct indicator: out of 50 industry queries, how many times are you cited?
A 0% rate means total invisibility. A rate of 60%+ indicates a dominant position. The average varies considerably by industry and company size.
Citation position
Not all mentions are equal. Being cited first ("I particularly recommend X") weighs more than being mentioned at the end of a list ("You could also consider Y"). Citation position refines the raw rate analysis.
AI Share of Voice
Share of voice relates your mentions to those of all players in your industry. If three competitors are each cited 30% of the time and you 10%, your share of voice is 10%. This indicator contextualizes your performance relative to the market.
Mention sentiment
A mention is only useful if it's favorable. "X is often criticized for its customer service" is a mention, but not the one you're aiming for. Sentiment analysis qualifies the nature of citations.
The 5-step methodology
Define the scope
An audit without a precise framework produces unusable data. Before launching anything, answer these questions:
- Which platforms? ChatGPT alone? ChatGPT + Claude + Perplexity + Gemini? The choice depends on your target audience.
- Which queries? Brand queries ("what do you think of [brand]")? Industry queries ("best [product] for [use]")? Informational queries?
- What depth? 20 queries for a quick audit? 100 for a complete analysis?
- Which competitors? Identify 3 to 5 competitors to monitor in parallel.
Build the query list
The audit quality depends on the relevance of tested queries. Three categories to cover:
- Brand queries: "What do you know about [brand]", "Reviews of [brand]", "[Brand] vs [competitor]"
- Industry queries: "Best [product] in France", "Recommend a [service] for [use case]", "[Category] comparison 2025"
- Problem queries: "How to solve [problem you address]", "Solution for [client pain point]"
Aim for a minimum of 30 queries for a significant audit, with balanced distribution across categories.
Collect the data
Collection can be manual or automated. Manually, you ask each query to different AIs and record responses. Automated, you use platform APIs.
Collection points of attention:
- Use fresh sessions (no conversation history)
- Test at the same times to avoid server load bias
- Activate web search on ChatGPT to capture real-time citations
- Keep complete responses, not just presence/absence of mention
Analyze the results
Once data is collected, structure the analysis around several axes:
- By platform: Compared performance on ChatGPT, Claude, Gemini, Perplexity
- By query type: Gaps between brand queries and industry queries
- By competitor: Who dominates on which queries?
- Qualitative analysis: How are you described when mentioned?
Cross dimensions to identify patterns: "Visible on ChatGPT for brand queries, absent on industry queries" is an actionable diagnosis.
Formulate recommendations
An audit only has value if it leads to concrete actions. For each finding, propose a recommendation:
- Absence on industry queries → create AEO-optimized content for these queries
- Dominant competitor → analyze their citation sources and replicate the strategy
- Negative mentions → identify the source and work on reputation
- Low Bing visibility → optimize Bing positioning (ChatGPT + Copilot impact)
Common mistakes to avoid
Mistake #2: Ignoring response variability. AIs don't always give the same response to an identical query. Test each query multiple times (minimum 3) to average results.
Mistake #3: Focusing only on ChatGPT. Depending on your audience, Claude or Perplexity may be more relevant. A single-platform audit gives a partial view.
Mistake #4: Neglecting qualitative analysis. Knowing you're mentioned isn't enough. How are you described? In what context? A mention in a list of "solutions to avoid" isn't a victory.
Mistake #5: Auditing once and forgetting. AI visibility evolves. Models are updated, sources change, competitors optimize. Plan regular audits (quarterly minimum).
Automating the audit
Manual collection quickly becomes time-consuming. Several approaches to automate:
Platform APIs. ChatGPT, Claude, and Perplexity offer APIs that allow submitting queries programmatically. You can script collection and parse responses automatically.
Specialized tools. Platforms like AI Labs Audit automate the entire process: query definition, multi-platform collection, analysis, scoring, and report generation.
Automation advantages:
- Unlimited query volume
- Audit repeatability
- Precise temporal comparisons
- Standardized client deliverables
Automate your AI visibility audits
Try AI Labs Audit: automatic collection on 4 platforms, scoring, customizable PDF reports.
Try for freeAudit report template
An effective audit report follows a logical structure:
- Executive summary — One-page synthesis: overall score, main findings, 3 priority recommendations
- Methodology — Tested platforms, number of queries, audit period
- Results by platform — Mention rate, average position, response examples
- Competitive analysis — Comparison with identified competitors
- Qualitative analysis — Mention sentiment, context, significant verbatims
- Recommendations — Priority actions with effort level and expected impact
- Appendices — Detail of tested queries and complete responses
Adapt the detail level to your audience. A decision-maker wants the executive summary. A marketing manager wants detailed recommendations. A technical SEO wants complete appendices.
Frequency and tracking
A one-time audit gives a snapshot. Regular tracking shows trends.
Initial audit: Complete, all platforms, 50+ queries. Establishes the baseline.
Follow-up audits (quarterly): Same queries, same methodology. Measures evolution and optimization impact.
Flash audits (monthly): Subset of 10-15 priority queries. Quick detection of significant changes.
Document each audit in a standardized format to facilitate temporal comparisons.
Key takeaways
AI visibility auditing is an emerging discipline, but the methodological fundamentals exist.
Define a clear scope before starting. Build a query list representative of your activity. Collect data systematically. Analyze across multiple dimensions. Transform findings into actionable recommendations.
Automate what can be automated to gain efficiency and repeatability. And establish a tracking rhythm to transform the one-time audit into continuous piloting of your AI visibility.
Companies that master this methodology have an advantage: they see what others ignore, and can act accordingly.
