Six technical planes, checked in the order that usually matters.
The VERIS audit is not a random checklist. It inspects six structural areas that affect whether AI systems can read, classify, trust, and reuse information about the business. Each plane maps back to the actual VERIS service stack.
- You want to understand what the audit is actually inspecting.
- You want to see how findings connect to the VERIS services.
- You want context before using the free audit form.
Every plane produces a clearer next step, not just a score.
Structured Data & Schema
Schema markup is the machine-readable declaration of what your business is. VERIS checks whether the correct schema type is present and whether the required properties are populated for the business category.
AI Crawler Access
robots.txt, sitemap, canonical tags, and redirect health. VERIS checks whether the relevant published AI and search agents can crawl the site and whether the site shows healthy public discovery signals.
AI-Readable Business Summary
llms.txt is an emerging convention for publishing a cleaner machine-readable summary of the business. VERIS checks whether it exists, whether it is current, and whether it aligns with the rest of the site.
Open Graph & Social Meta
Open Graph metadata controls how pages resolve in link previews and summary cards. VERIS checks the core fields, whether the preview resolves, and whether the description is specific enough to support better extraction.
Page Speed & Technical Signals
Core Web Vitals, render-blocking resources, image behavior, and technical hygiene. VERIS looks for the issues most likely to reduce crawl reliability and page usefulness.
Entity Clarity & Public Trust Signals
AI systems build trust by cross-referencing the business across sources. VERIS checks NAP consistency, sameAs alignment, and whether the public identity is coherent enough to support recommendation and citation.
Findings are classified by what they block, not by how dramatic they sound.
Severity is about implementation order. A blocked crawler or missing schema type usually matters before a smaller metadata issue, because it affects the site’s ability to be understood at all.
Directly prevents AI systems from accessing or classifying the business. These are the first issues VERIS expects to fix.
A significant structural gap. The site may still exist publicly, but it is materially weaker for AI extraction or trust.
A quality or support issue that reduces extraction reliability, clarity, or technical trust.
A smaller improvement that still matters, but does not usually lead the implementation order.