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 ten weighted families (avs-v3), 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 ten family scores (avs-v3), 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.

Content Extractability Grader — AI Answer Block Analysis

Free tool to grade how well AI models can extract answers from your page. Analyzes heading hierarchy, FAQ patterns, answer-block density, and content structure.

TLDR

The Content Extractability Grader evaluates heading hierarchy, answer-block density, FAQ patterns, structured lists, and content density to assess how well AI models can extract answers from a page.

What the extractability grader evaluates

This tool counts H1/H2/H3/H4 headings and scores hierarchy structure, measures word count and paragraph count, detects FAQ-like question patterns, counts ordered and unordered lists, tables, definition lists, and details elements, and computes average sentence length.

It flags pages that lack a single H1, have no H2 sections, have low word count, few substantive paragraphs, no lists, no answer-sized content blocks, or long average sentence length.

What extractability does not prove

Content extractability measures structural readiness — how well content is organized for machine parsing. It does not predict citation behavior. A page with high extractability may still be omitted from AI answers if it lacks authority signals, entity clarity, or external verification.