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.

FAQ Schema for AI Answer Placement | AiVIS Cite Ledger

FAQ schema directly tells AI models: 'Here are questions and here are authoritative answers.' It's the single highest-impact signal for appearing in AI-generated answer boxes.

Why FAQ Schema Has Maximum Impact

AI answer engines (Perplexity, Google AI Overviews, ChatGPT) actively look for FAQ schema to find pre-formatted Q&A content they can cite directly.

Pages with FAQ schema provide extraction-ready answers that AI models can use without interpretation, reducing hallucination risk and increasing citation confidence.

FAQ Schema Implementation

Use FAQPage type with mainEntity array. Each entry is a Question type with name (the question) and acceptedAnswer containing the answer text.

FAQ schema must match visible on-page content. Don't add schema for invisible or hidden Q&A pairs.

Keep answers concise and self-contained: 2-4 sentences that fully answer the question without requiring external context.

FAQ Content Strategy

Write questions in natural conversational language matching how people ask AI models.

Target questions your audience actually asks, not marketing-driven questions. 'How does X work?' > 'Why is X the best?'.

Add FAQ sections to every content page where questions naturally arise, not just dedicated FAQ pages.

Frequently Asked Questions

How many FAQs should I add per page?: 3-8 per page is optimal. Enough to provide value without FAQ-stuffing. Each should address a genuine question your audience asks.
Can I reuse FAQ schema across pages?: Each page should have unique FAQs relevant to that page's topic. Duplicate FAQ schema across pages reduces the value of each.
Does FAQ schema work on product pages?: Yes, product pages benefit greatly from FAQ schema addressing common buyer questions, shipping details, and compatibility information.