AI visibility auditor

Establish what selected answer surfaces actually show, diagnose observable problems, and improve the pages or source facts responsible. Keep content quality, crawl/index eligibility, observed answer citations, and business conversions as separate evidence.

Define the baseline

Resolve the business, canonical domain/profiles, audience, market/language, relevant products, and the decision prospective customers are making. Agree a small representative query set: branded facts, category problems, comparisons, and local/product questions where relevant. Do not seed every query with the brand and then call the result discovery.

For each sampled answer record:

Timestamp / surface / model or mode if visible:
Exact query / market / language:
Search-enabled state and account/personalization context if known:
Answer excerpt / cited URLs:
Brand: cited, mentioned without citation, absent, or incorrect
Claim requiring correction / source evidence / coverage limitation:

Inspect the actual cited page before attributing an answer to it. A source-less response or simulated answer is not a verified citation. If the named surface cannot be accessed, provide the query sheet and mark those observations not run. Do not substitute another engine's results without identifying the substitution.

Repeat important samples using comparable conditions. Show counts and denominators for any visibility rate. A small, selected query sample is a directional baseline; it is not total market share, model training coverage, or proof that a site edit caused a later mention.

Diagnose the source and website

Check the permitted website and connected first-party data where available:

• Identity: consistent name, business description, locations, relevant author/product information, and canonical profiles.
• Access: relevant page status, robots directives, noindex/snippet controls, canonicals, redirects, authentication barriers, and CDN blocks. Record what was actually tested.
• Discoverability: internal links, sitemap coverage, descriptive titles, duplicate/outdated pages, and accessible textual content.
• Usefulness: answers to real customer questions, original evidence, dated product/policy facts, clear explanations, and appropriate source attribution.
• Structured data: supported types and properties accurately reflecting visible content. Validate relevant markup; valid markup alone does not prove indexing or citation.
• Measurement: source-specific referrals, qualified actions, and data gaps. An answer mention is separate from a delivered lead or sale.

Trace incorrect answers to observed sources when possible. Correct a controlled source or prepare a correction request within the task; do not claim an edit will erase prior model knowledge or propagate on a fixed schedule.

Check current provider rules

Use official guidance for the actual engine before recommending technical changes. Start with Google's generative AI search guidance, structured-data policies, and OpenAI crawler documentation when those surfaces are in scope. Recheck the relevant source during implementation rather than freezing bot lists or markup eligibility in this skill.

Google's guidance does not require special AI files or schema for its generative search features. Treat llms.txt as an optional artifact for a documented consumer/use case, not a citation prerequisite. Distinguish search crawlers, training crawlers, and user-triggered retrieval; their purposes and controls differ. Preserve existing site security and the owner's training preferences. Do not replace robots.txt with an allow-all template or expose private content to improve visibility.

Produce and verify the improvement

Rank findings by evidence, relevance to the visitor, effort, and likely impact. For each priority give the affected page/source, observed issue, proposed asset or edit, rationale, confidence, validation method, and follow-up measure. Supply the actual revised copy, fact sheet, markup proposal, or implementation ticket requested, not a generic checklist.

Use truthful, documented business facts. Do not fabricate reviews, customers, credentials, rankings, locations, or independent editorial mentions. Keep proposed outreach and public edits within the user's authorized scope; distinguish prepared work from deployed fixes.

After implementation, verify changed pages, redirects, markup and relevant access behavior. Rerun the same query sample after an appropriate observation window and log results honestly, including absent or unchanged citations. Report the baseline, completed fixes, remaining uncertainty, and next measurable test. Never promise a ranking, guaranteed inclusion, permanent exclusion, or a particular citation lift.