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.

AI Is Hallucinating About My Brand | AiVIS Cite Ledger

When AI models don't have reliable data about your brand, they fill gaps with plausible-sounding fiction. This isn't malice, it's the inevitable result of insufficient structured data.

Why AI Hallucinations Happen

AI language models generate text by predicting likely next tokens. When they lack specific data about your brand, they extrapolate from patterns in their training data, creating plausible but false information.

Common hallucinations include wrong founding dates, incorrect product descriptions, fabricated reviews, and misattributed features from competitor products.

How Structured Data Prevents Hallucinations

Comprehensive Organization schema gives AI models verified brand facts: name, founding date, CEO, address, products, description.

Product schema provides accurate pricing, features, and specifications that anchor AI responses in real data.

FAQ schema pre-answers common questions with your approved language, reducing the need for AI to generate its own (potentially wrong) answers.

Corrective Actions

Add comprehensive Organization and Brand schema to your homepage with every verifiable fact about your company.

Create a detailed llms.txt file that provides curated, accurate context about your brand for AI models.

Publish authoritative content on your own site answering the questions AI models commonly get wrong about your brand.

Monitor AI responses about your brand with AiVIS Cite Ledger citation testing to catch hallucinations early.

Frequently Asked Questions

Can I stop AI from hallucinating about my brand?: You can significantly reduce it by providing comprehensive, machine-readable data. More structured data = less room for AI to guess = fewer hallucinations.
How do I find AI hallucinations about my brand?: Use AiVIS Cite Ledger's citation testing feature to ask AI models about your brand and compare their responses to your actual information.
Should I contact AI providers about wrong information?: You can, some providers have correction channels. But the most effective approach is providing better source data through structured markup and authoritative content.