AI Visibility Audit

Billions of queries flow through AI assistants like ChatGPT and Claude each day, yet most websites never appear in the results. This audit addresses that gap by measuring how visible websites are to AI systems.

The service targets website owners and marketers concerned with AI discoverability. Rather than guessing why a website might be excluded from AI answers, the audit pinpoints the exact obstacles blocking visibility.

The methodology is comprehensive. The tool checks whether AI crawlers like GPTBot have permission to index the site, examines identity files that AI systems read first (such as ai.json and llms.txt), and evaluates the technical infrastructure underlying the website. It then tests what AI models actually say when asked about the brand, comparing that against competitor mentions. The audit scores visibility across eight dimensions—retrieval readiness, search presence, technical performance, AI sentiment, mention position, and others—with each component weighted as part of a larger 100-point scale.

What distinguishes this service is its specificity. Rather than offering generic SEO advice, it traces the path an AI assistant takes when researching a topic: verifying crawler access, identifying the company, and finding quotable content. When visibility is low, the report identifies which step failed, making remediation concrete rather than speculative.

The platform walks through six stages during an audit. It fetches the live website and samples surrounding content, reads the site's metadata and schema structure, assesses whether retrieval is actually possible, runs live queries across answer engines, scores the results, and generates an action plan with specific files to implement. The audit can be run once as a baseline or repeated weekly for ongoing monitoring.

The action plan component focuses on implementation. Rather than abstract recommendations, it specifies which files to create or modify—from standard files like robots.txt to AI-specific ones like llms.txt. This makes the findings immediately actionable for technical teams.

The founding team frames this work within a broader mission to help organizations use AI to reduce repetitive operational tasks and improve how teams communicate with customers. For businesses relying on AI-driven discovery, the audit addresses a critical but often overlooked gap: being present in the systems people actually consult when making decisions.