AX Audit
Evaluate how artificial intelligence agents perceive an organization's web presence. The diagnostic produces an instantaneous AX (Agent Experience) score and actionable recommendations for improving representation across AI discovery surfaces.
What we check in the audit
llms.txt
AI info file
AI Plugin
Plugin manifest
JSON-LD
Structured data
robots.txt
AI bot rules
OpenAPI
API spec
Semantic
HTML structure
AX Audit Form
How the AX Audit operates
The diagnostic performs comprehensive validations to evaluate the organization's preparation for the AI discovery ecosystem.
Submit the URL
The organization's website URL is submitted to initiate the diagnostic.
Automated analysis
The diagnostic validates the presence of llms.txt, AI plugin manifests, structured data, semantic markup, and additional discovery instrumentation.
Receive the diagnostic
The organization receives an immediate AX score accompanied by formally documented recommendations for improvement.
Frequently asked questions
What is an AX (Agent Experience) audit?
An AX audit evaluates the extent to which an organization's website is engineered to be understood and accurately represented by artificial intelligence agents such as ChatGPT, Claude, Perplexity, and analogous large language models. The diagnostic assesses the technical elements that enable AI systems to interpret the content with operational fidelity.
What does this complimentary diagnostic verify?
The diagnostic verifies the presence and structural conformance of llms.txt files, ai-plugin.json manifests, robots.txt directives applicable to AI crawlers, JSON-LD structured data, OpenAPI specifications, semantic HTML, metadata, and sitemap availability.
Is the diagnostic genuinely complimentary?
The baseline AX audit is provided without charge and without registration requirements. Comprehensive optimization and implementation are delivered through the Durani AI Partner engagement.
Why does AX optimization matter for the organization?
As conversational AI assistants emerge as primary information channels, an optimized AX ensures that the organization is correctly cited, recommended, and identified by the user populations that engage with these systems daily. Inadequate AX results in misrepresentation or invisibility across AI-mediated discovery.
How is the AX score improved?
The diagnostic produces specific recommendations for improvement. Principal areas include the implementation of llms.txt, the introduction of structured data through JSON-LD, the optimization of semantic HTML, and the configuration of robots.txt to permit AI crawler access. The Durani AI Partner engagement provides full implementation.
Prepare the organization for the conversational discovery era
Inadequate representation across AI agents results in incorrect characterization or omission. Professional Agent Experience optimization ensures accurate representation across all major AI platforms.
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