AI Visibility Audit

What an AI Visibility Audit Is

An AI visibility audit answers a simple question: when your buyers ask AI models for recommendations in your category, does your company appear? The audit provides a baseline measurement of your current AI presence and a roadmap for improvement.

Unlike traditional SEO audits that analyse rankings, backlinks, and technical health, an AI visibility audit focuses specifically on how language models represent and recommend your business. The two assessments overlap in places but measure different things entirely.

How It Works

The audit process typically has four phases. Query development identifies the questions your buyers actually ask AI models, such as “Who are the best management consultants for digital transformation?” or “Which law firms specialise in technology M&A in the UK?”

Multi-model testing runs these queries across ChatGPT, Claude, Gemini, Perplexity, and other relevant AI platforms. Each response is documented, noting whether your company is cited, how it is described, and what context surrounds the mention.

Competitive analysis runs the same queries and documents which competitors appear, how they are positioned, and what content or signals drive their visibility.

Technical review examines the factors that influence AI citation: structured data implementation, llms.txt presence, content structure, entity clarity, and knowledge graph presence.

What It Reveals

The audit typically reveals gaps between a company’s actual expertise and its AI-visible presence. Firms that are well-known in their industry and rank well on Google are often surprised to find they are absent from AI recommendations. The audit explains why and shows what to change.

Acting on Results

The output is a prioritised action plan. Quick wins like adding llms.txt or FAQ schema might take days. Content restructuring might take weeks. Building topical authority takes months. The audit prioritises by impact and effort so resources go to the changes that matter most.