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.

What Is an AI Search Visibility Audit? | AiVIS Cite Ledger Blogs

By Founder, AiVIS Cite Ledger · 2026-06-25 · 10 min read · METHODOLOGY

A traditional SEO audit tells you how Google sees your site. An AI search visibility audit tells you how ChatGPT, Claude, Perplexity, and Gemini see it. The signals are different. So is the fix.

Key Takeaways

An AI search visibility audit measures how ChatGPT, Claude, Perplexity, and Gemini read, verify, and cite your site — distinct from a traditional SEO audit.
An SEO audit tells you how Google indexes your site; an AI visibility audit tells you whether AI models can extract claims from it and whether they actually cite you.
The avs-v3 ten scoring families in an AiVIS audit: schema & structured data, entity & heading signals, authority & EEAT, meta tags & open graph, content depth, crawlability & bot access, renderability & page speed, citation signal quality, indexability & link graph, and security & trust signals.
Citation presence testing is a live probe — AiVIS sends actual queries to AI models and records whether your site is named in the response, not whether it could theoretically be.
AiVIS.biz is the only AI visibility audit platform with an immutable Cite Ledger record: every citation finding is time-stamped and non-alterable for compliance and benchmarking.

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What Is an AI Search Visibility Audit?

The Definition

An AI search visibility audit is a structured assessment of how AI models — ChatGPT, Claude, Perplexity, Gemini — read, interpret, and cite a website or entity when answering user queries.

It is distinct from a traditional SEO audit in purpose, methodology, and output. An SEO audit evaluates Google ranking signals. An AI search visibility audit evaluates AI citation signals.

The two audits can produce completely different diagnoses for the same site. A site that passes an SEO audit with high marks can fail an AI visibility audit entirely. A site that has poor Google rankings can have strong AI citation presence if its entity and extraction signals are well-built.

What an AI Search Visibility Audit Measures

A comprehensive AI search visibility audit covers ten scoring families (avs-v3):

**1. Schema & Structured Data**

Is your machine-readable metadata implemented, consistent, and complete? This includes Organization, Article, FAQPage, and other JSON-LD blocks that give models explicit facts about your entity and content.

**2. Entity & Heading Signals (Entity Clarity)**

Does your content and schema declare the canonical entity form clearly (name, role, product), and do headings and intro paragraphs support extractable entity claims?

**3. Authority & EEAT**

Do external signals and on-site trust markers (citations, author credentials, third-party references) establish authority for claims that models might cite?

**4. Meta Tags & Open Graph**

Are meta titles, descriptions, and OG tags well-formed and aligned with canonical content to improve extraction and attribution fidelity?

**5. Content Depth**

Does the page provide sufficient depth and answer-complete sentences that models can extract as citable claims?

**6. Crawlability & Bot Access**

Have you granted AI retrieval agents proper access via robots.txt and site indexi

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