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.

No Schema Markup Detected on Your Site | AiVIS Cite Ledger

An AiVIS Cite Ledger audit found no schema markup on your site. This means AI models can read your text but have no structured way to verify authorship, classify content, or generate reliable citations.

What Zero Schema Means for AI

Without schema markup, AI models treat your content as undifferentiated text. They can't distinguish your article from a comment section, your product from a sidebar widget.

Citation engines like Perplexity and ChatGPT with browsing prefer sources with machine-verifiable structure, schema lets them confirm what they're citing.

Priority Schema to Add First

Organization: tells AI who published the content. Add this site-wide, typically in the global layout or header.

Article: identifies blog posts, news, and editorial content with author, date, and headline metadata.

FAQ: marks Q&A sections for direct inclusion in AI answer generation. High impact for citation likelihood.

Quick Implementation Guide

Start with a single JSON-LD block in your homepage's <head> containing Organization schema. This immediately establishes your entity identity.

Add Article schema to your blog post template, most CMS platforms and frameworks support this through plugins, modules, or custom components.

Add FAQ schema to any page with a Q&A section. This is one of the highest-impact signals for AI answer placement.

Frequently Asked Questions

Is schema markup hard to add?: No. JSON-LD schema is just a JavaScript object in a script tag in your page's head. Most CMS platforms have plugins that generate it automatically.
How many schema types do I need?: Start with Organization (site-wide), Article (content pages), and FAQ (Q&A pages). Add Breadcrumb, Product, and HowTo as relevant.
Will adding schema immediately improve AI visibility?: Improvement depends on crawl timing, but schema gives AI models immediate verifiable structure. Run a follow-up AiVIS Cite Ledger audit to confirm deployment.