What exactly is AiVIS Cite Ledger?

AiVIS Cite Ledger is an evidence-backed AI citation audit and remediation system. It measures how AI models, ChatGPT, Perplexity, Claude, and Google AI, interpret, trust, and cite your web content. The result is a 0–100 CITE LEDGER score across seven weighted dimensions, with BRAG evidence-linked findings and prioritized fixes.

How is this different from Semrush, Ahrefs, or Clearscope?

Traditional SEO tools optimize for keyword ranking in traditional search results. AiVIS Cite Ledger audits citation readiness, the specific signals that determine whether AI answer engines retrieve and cite your content. A page can rank #1 on Google and be completely invisible to AI answer synthesis.

What is BRAG and how does it make findings trustworthy?

BRAG stands for Based-Retrieval-Auditable-Grading. It ties every audit finding to a real, traceable element on your page, a specific heading, schema block, meta tag, or content section, each carrying a unique evidence ID. You can open the element, verify the finding, and recheck it after a fix. No black boxes.

Is the free tier genuinely useful, or just a teaser?

The Observer (free) plan gives you a full composite CITE LEDGER score and all seven dimension letter grades, so you can see exactly where you stand and which categories are hurting you. BRAG evidence IDs and prioritized fix packs require a paid plan. Three audits per month, no credit card.

AI Visibility for Media & Publishing | AiVIS Cite Ledger

Media companies live or die by citations. When AI models answer factual questions, they cite publishers with the strongest Article schema, author entities, and organizational trust signals.

Media in the AI Era

AI-generated answers frequently cite news articles and publications as sources. Publishers with comprehensive Article schema, verified author entities, and strong Organization markup maintain citation share.

The publishers who invest in structured data now capture the AI citation traffic that others lose.

Media Schema Priorities

NewsArticle/Article: headline, author, datePublished, dateModified, description, articleBody (or wordCount), and publisher.

Person schema for every journalist/author with credentials, beat, and publication history.

Organization/NewsMediaOrganization: founding date, editorial policy, corrections policy, and ownership transparency.

FAQ schema on explainer articles and topic hub pages.

Publisher Content Strategy

Evergreen explainer content with regular updates earns ongoing AI citations beyond breaking news.

Topic hub pages that aggregate related coverage help AI understand your depth on specific subjects.

Structured data on opinion vs news content helps AI correctly attribute the content type.

Frequently Asked Questions

Do paywalled articles hurt AI visibility?: Partially, AI crawlers may not access full paywalled content. Consider structured-data-rich free summaries or metered access for AI discoverability.
How important are author entities for media?: Critical. AI models use author entities to assess expertise and trustworthiness. Every bylined article should connect to an author Person entity.
Should opinion content have different schema?: Yes, distinguishing opinion from news via schema helps AI models correctly attribute and present your content.