Six technical planes. Every gap identified.
The VERIS audit checks six distinct layers of AI search readiness. Each plane has specific failure modes that prevent AI systems from reading, classifying, or recommending a business. Here is what each one checks and why it matters.
Structured Data & Schema
Schema markup is the machine-readable declaration of what your business is. VERIS checks whether the correct schema type is present (LodgingBusiness, Restaurant, Physician, etc.) and whether all required properties are populated. A business without correct schema cannot be classified by AI systems.
AI Crawler Access
robots.txt, sitemap, canonical tags, and redirect chains. VERIS checks whether published AI and search agents such as OAI-SearchBot, GPTBot, PerplexityBot, and ClaudeBot can crawl the site, and whether the site has healthy public discovery signals in Google and Bing.
AI-Readable Business Summary
llms.txt is an emerging convention that can give language models and AI-assisted search products a direct summary of the business. Without it, systems rely entirely on scattered page content, schema, and external citations to reconstruct context.
Open Graph & Social Meta
Open Graph metadata is what AI systems and social platforms use to generate link previews and business descriptions. VERIS checks every og: and twitter: property, including whether the og:image resolves at the correct dimensions.
Page Speed & Technical Signals
Core Web Vitals, render-blocking resources, image optimization, and SSL. While not a direct AI ranking factor, poor technical performance signals reduce crawl priority and trust. VERIS identifies the most impactful issues without requiring a full performance audit.
Entity Clarity & E-E-A-T
AI systems build trust in businesses by cross-referencing multiple sources. VERIS checks NAP consistency across Google, Bing, TripAdvisor and category platforms, plus Knowledge Panel status and aggregateRating accuracy in schema.
How findings are classified.
Directly prevents AI systems from accessing or classifying the business. Immediate business impact.
Significant structural gap. Material limitation on AI visibility.
Reduces AI answer quality and recommendation precision.
Minor improvements. Incremental AI readiness gains.