What does an AI visibility audit include?

A comprehensive AI visibility audit tests your business across 10 AI platforms using 15 industry-specific queries, analyzes AI sentiment about your brand, benchmarks you against competitors, detects AI hallucinations about your business, and scores your website on 12 GEO optimization signals.

How often should I run an AI visibility audit?

Monthly audits are the industry standard. AI models update regularly, competitors are constantly optimizing, and your score can change due to factors outside your direct control. Monthly tracking gives you the data to respond proactively rather than reactively.

What is a good AI visibility score?

A score above 60 is considered strong and means you're regularly recommended by AI platforms. Most businesses starting their GEO optimization journey score between 15 and 35. Reaching 60+ within 6 months of optimization is a realistic target for most local service businesses.

AI Visibility Audit Explained: What It Tests, What Your Score Means, and What to Do

Before you can improve your AI visibility, you need to know where you stand. An AI visibility audit gives you a precise, measurable baseline — and a prioritized action plan based on your actual gaps, not generic advice.

Here’s exactly what a comprehensive AI visibility audit tests, how each component is scored, and what to do with the results.

Component 1: Platform Visibility Testing

A rigorous audit queries your business across multiple AI platforms using 15+ real customer queries in 5 intent categories:

Direct queries — “Tell me about [Business Name]”
Service queries — “Best [service] in [city]?”
Problem queries — “I need a [service] provider in [city]”
Comparison queries — “Compare [service] companies in [city]”
Trust queries — “Is [Business Name] reliable?”

Each platform scores based on how often your business is mentioned in responses to these queries. The aggregate across all platforms gives your overall visibility score.

Component 2: Sentiment Analysis

Not all mentions are positive. The audit analyzes the tone of AI responses about your business — positive (AI actively recommends you), neutral (mentioned without preference), or negative (AI discourages customers from choosing you). Negative AI sentiment is an urgent flag requiring immediate content and reputation intervention.

Component 3: Competitor Benchmarking

The same 15 queries are run against your top competitors, producing a side-by-side score comparison. This shows you exactly which competitors AI prefers and by how much — quantifying the revenue gap and identifying which competitive gaps to close first.

Component 4: Hallucination Detection

AI sometimes makes up facts about businesses — wrong addresses, invented credentials, incorrect services. The audit screens AI responses for potentially fabricated claims about your business. Hallucinations can actively harm your reputation and require immediate content correction to override.

Component 5: Website GEO Score

Independent of how AI currently sees you, the GEO audit analyzes your website for the 12 technical signals that enable AI to cite you: schema markup, FAQ content, entity keywords, NAP visibility, sitemap, and semantic HTML structure. This score tells you where your technical optimization gaps are.

Component 6: ROI Projection

Based on your current score, target score, and average client value, the audit calculates the estimated monthly revenue impact of full optimization — giving you a concrete business case for the investment.

What to Do With Your Score

Audit results map directly to a prioritized action plan:

Critical GEO issues (schema missing, no FAQ): Fix within 7 days — these are the highest-leverage technical changes
Negative sentiment: Address within 30 days with targeted content and review campaigns
Competitor gap: Use the 90-day content and citation roadmap to close the gap systematically
Hallucinations: Correct immediately with authoritative content publishing

A baseline audit should be repeated monthly to track progress, catch score drops early, and measure the impact of optimization work against a consistent benchmark.