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.

Missing FAQ Schema - Your Q&A Is Invisible to AI | AiVIS Cite Ledger

Your site has Q&A content, but without FAQ schema, AI models see it as regular paragraphs. FAQ markup is one of the highest-impact signals for AI answer inclusion.

Why FAQ Schema Has High Impact

FAQ schema explicitly tells AI models 'this is a question and this is its answer.' Without it, AI must infer Q&A structure from headings and formatting, an unreliable process.

Pages with FAQ schema are significantly more likely to appear in AI-generated featured answers and citation panels.

Adding FAQ Schema

Wrap your Q&A content in FAQPage JSON-LD schema. Each question-answer pair becomes a mainEntity with Question type and acceptedAnswer.

The FAQ schema should match visible on-page content. Don't add FAQ schema for questions that aren't visible to users, this is considered misleading.

FAQ Best Practices for AI

Write questions in natural language matching how people actually ask. 'How do I add JSON-LD?' instead of 'JSON-LD Implementation Guide'.

Keep answers concise but complete, 2-4 sentences that directly answer the question. AI models prefer extractable, self-contained answers.

Add FAQ sections to every content page where questions naturally arise. Product pages, service pages, and how-to guides all benefit.

Frequently Asked Questions

How many FAQs should I add per page?: 3-8 FAQs per page is ideal. Enough to provide value without overwhelming the content. Each should be genuinely useful to your audience.
Does FAQ schema guarantee AI answer placement?: No, but it significantly increases your odds. FAQ schema is one of the strongest signals for AI answer inclusion, especially for direct-answer queries.
Can I use FAQ schema on non-FAQ pages?: Yes, as long as the Q&A content is visible on the page. Many product pages, service pages, and blog posts naturally contain FAQ-worthy content.