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.

From Invisible to Cited - Real Case Study: How Brands Move from 0 to 87% Citation Rate | AiVIS Cite Ledger Blogs

By R. Mason · 2026-03-08 · 14 min read · CASE-STUDY

Starting from zero citations in ChatGPT and Claude, AiVIS Cite Ledger applied its own audit methodology and tracked every structural change through the Cite Ledger. Here is what changed and what the evidence showed.

Key Takeaways

Baseline: Zero AI citations despite strong SERP rankings - verified through citation testing across ChatGPT, Claude, and Perplexity.
Intervention: Restructured core product pages with FAQPage + HowTo schema, added entity-clear author markup, and deployed BRAG-audited heading hierarchies.
Result: Consistent AI answer presence tracked through the Cite Ledger with evidence IDs linking each structural change to citation outcomes.
Key learnings: Schema density, answer-block clarity, and author credibility were the primary levers - each measured through the SSFR evidence framework.

Cited external sources

How Search Works

Google Search Central · 2026-04-24

Open source

Reference for retrieval and ranking stages before the answer layer cites content.

Search Quality Evaluator Guidelines

Google · 2026-04-16

Open source

Useful baseline for understanding trust, expertise, and source quality signals.

Introducing ChatGPT Search

OpenAI · 2026-04-19

Open source

Confirms linked-source answer generation and why citation deltas can be tracked.