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.

Schema Markup Validator — AI Citation Readiness Check

Free tool to validate your structured data (JSON-LD, OpenGraph, Twitter Cards) for AI citation readiness. See what AI models can extract from your page.

TLDR

The Schema Markup Validator checks whether a page declares JSON-LD structured data, which schema types are present, and whether OpenGraph, Twitter Card, meta description, and canonical signals are complete.

What the schema validator checks

This tool parses a page for JSON-LD blocks, flattens @graph arrays, and reports which schema types are present (FAQPage, Article, Organization, Product, HowTo, BreadcrumbList, WebSite, WebPage). It also extracts OpenGraph tags, Twitter Card tags, meta description, and canonical URL.

It flags missing high-value types, broken JSON syntax, duplicate declarations, and OpenGraph property gaps that reduce extraction quality.

What schema validity does not prove

Schema markup increases extraction confidence but does not guarantee that AI systems will cite a page. Citation decisions depend on additional factors including content depth, entity authority, competitor landscape, and model-level inference that extend beyond structured data alone.