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.

Schema.org Markup for AI Engines | AiVIS Cite Ledger

Schema.org is the vocabulary that powers structured data. AI models use it to classify, verify, and cite your content with confidence.

What is Schema.org?

Schema.org is a shared vocabulary for structured data on the web, maintained by Google, Microsoft, Yahoo, and Yandex. It defines types (Article, Organization, Product) and properties (name, author, datePublished) that AI models understand.

Priority Schema Types for AI

Organization: your publisher identity. Essential for entity recognition and trust.

Article: for editorial content. Connects content to authors, dates, and topics.

FAQPage: marks Q&A content for direct extraction by AI answer engines.

Product: for e-commerce. Provides verified pricing, availability, and features.

BreadcrumbList: shows hierarchical position, helping AI understand content relationships.

Implementation Best Practices

Use JSON-LD format for all Schema.org markup. It's the format AI models prefer and the easiest to maintain.

Ensure schema data matches visible page content. AI models cross-reference schema claims against page text.

Validate your schema output with Google's Rich Results Test and an AiVIS Cite Ledger audit.

Frequently Asked Questions

How many schema types should I use?: Start with Organization (site-wide) and Article or Product (per page type). Add FAQ, Breadcrumb, and Person as relevant.
Does schema guarantee AI citations?: No, but it significantly improves machine verifiability, which AI models factor into citation decisions.
Is Schema.org the same as JSON-LD?: No, Schema.org is the vocabulary (types and properties). JSON-LD is the format used to express Schema.org markup in HTML.