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.

How LLMs Parse Your Content - Technical Breakdown of ChatGPT, Claude, and Perplexity Extraction | AiVIS Cite Ledger Blogs

By R. Mason · 2026-03-10 · 12 min read · TECHNOLOGY

Most teams have no idea what LLMs actually evaluate when parsing their site. Here\

Key Takeaways

LLMs use structured data (JSON-LD, microdata) as the primary extraction signal.
Answer engines tokenize differently per model: ChatGPT ~4k context, Claude ~100k, Perplexity varies.
Answer blocks (FAQPage, HowTo, Question-Answer) are often prioritized over unstructured body copy for extraction.
URL normalization, canonicalization, and robots.txt directives are evaluated before content parsing.

Cited external sources

Introducing ChatGPT Search

OpenAI · 2025-11-20

Open source

Evidence that synthesized answers select and cite sources rather than exposing full SERPs.

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

arXiv · Patrick Lewis et al. · 2025-11-20

Open source

Background for retrieval, chunking, and grounded answer assembly.

Search Central documentation on structured data

Google Search Central · 2026-04-16

Open source

Reference for JSON-LD and machine-readable content signals.