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.

How to Audit AI Answer Readiness | AiVIS Cite Ledger

An AI answer readiness audit measures whether your content is structurally prepared for extraction by ChatGPT, Perplexity, Gemini, Claude, and other answer engines. Here is what the audit evaluates and why.

What an AI readiness audit measures

Unlike a traditional SEO audit focused on rankings and backlinks, an AI readiness audit evaluates extraction fidelity: can AI models access your page, parse the content, identify the publisher, and reproduce claims accurately?

AI visibility audits ten scoring families (the scoring model): Schema & Structured Data (18%), Entity & Heading Signals (14%), Authority & E-E-A-T (12%), Meta Tags & Open Graph (10%), Content Depth (10%), Crawlability & Bot Access (8%), Renderability & Page Speed (8%), Citation Signal Quality (8%), Indexability & Link Graph (6%), Security & Trust Signals (6%). Each family targets a specific extraction failure mode.

How the evidence protocol works

Every finding in an AI visibility audit includes an evidence ID. This ID links the finding to the source signal on your page, the extraction rule that triggered it, and the scoring dimension it affects.

The protocol eliminates speculative recommendations. If the audit cannot observe a signal on the page, it does not generate a finding for it. Every recommendation maps to verified crawl evidence.

Running your first audit

Enter any public URL into the audit. The system crawls the page via headless browser, extracts all structural signals, scores ten scoring families (the scoring model), and delivers a composite score from 0 to 100 with prioritized fix recommendations.

The free audit provides the score and top blockers. Paid plans provide all recommendations with implementation code and evidence detail.

Frequently Asked Questions

How often should I audit my pages?: After any significant content or structural change, and at minimum monthly. AI model behavior evolves, so periodic re-audits catch both site regressions and model-side changes.
Can I audit competitor pages?: Yes. On advanced audit plans, you can add competitor domains and compare extraction readiness across shared topics.