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.

Why Structured Data Matters for AI Citations | AiVIS Cite Ledger

AI models can read your content without structured data. But they cannot reliably cite it. JSON-LD is the bridge between extraction and attribution.

Extraction vs attribution

AI models can extract text from any HTML page. But extraction is not citation. Citation requires attribution, the model must know who published the content, when, and in what context. JSON-LD structured data provides this attribution layer.

Without Organization schema, the model cannot determine the publisher. Without datePublished, it cannot establish temporal priority. Without author metadata, it cannot attribute claims to a specific person or entity.

Which schema types matter most for citation

Organization: establishes entity identity, connects to verified profiles via sameAs. Article/BlogPosting: declares content type, author, publication date. FAQ: marks Q&A content for direct extraction. BreadcrumbList: provides hierarchical context. Person: identifies content authors with credentials and profiles.

AiVIS Cite Ledger evaluates 18+ schema.org types as part of the Schema & Structured Data dimension.

The compounding effect

Pages with one schema type see moderate improvement. Pages with Organization + Article + FAQ + BreadcrumbList see significantly higher extraction fidelity and citation rates. The combination creates a complete entity context that AI models can use for confident attribution.

Frequently Asked Questions

Is JSON-LD the only format AI models use?: JSON-LD is the preferred format. Microdata and RDFa are technically parseable but less reliably processed by AI extraction pipelines. Use JSON-LD for maximum compatibility.
Can structured data alone improve my AiVIS Cite Ledger score?: Schema & structured data is 20% of the composite score. Adding comprehensive JSON-LD can improve your score significantly, especially if you are starting from zero structured data.