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.

Score Fix AutoFix PR: How AI Opens Pull Requests to Fix Your Visibility | AiVIS Cite Ledger Blogs

By R. Mason · 2026-03-24 · 7 min read · TECHNOLOGY

You got the audit. You see the problems. Now what if the platform opened a PR with the actual fix instead of just telling you what is wrong?

Key Takeaways

Score Fix generates actual code changes and opens GitHub PRs with audit-based fixes instead of just producing recommendations.
Credit-based pricing: $299 one-time add-on with 250 repair credits, 10-25 credits per PR depending on fix complexity.
Common automated fixes: schema markup, meta tags, heading restructuring, alt text generation, structured data repair.
PRs are never auto-merged. Teams review, comment, approve, or reject with 80% credit refund on rejection.
Post-merge monitoring tracks score delta and creates direct attribution: audit finding to code fix to visibility improvement.

Article

Every audit tool in the market does the same thing. Scan your site. Generate a list of problems. Hand you a PDF. Done.

Then it becomes your problem. You take the recommendations to your dev team. They look at it. They add it to the backlog. It sits for six weeks. Maybe it gets done. Maybe it does not. Meanwhile your competitors are getting cited and you are not.

Score Fix AutoFix PR changes this entire dynamic. Instead of telling you what is wrong, it generates the actual code changes and opens a pull request on your GitHub repository. The fix is ready for review and merge. Not a suggestion. The actual code.

How It Works

The process starts after a regular AiVIS Cite Ledger audit. You have your visibility score, your SSFR evidence breakdown, and your recommendations. For pages where the recommended fixes involve code changes like adding schema markup, fixing heading hierarchy, or updating meta tags, Score Fix takes over.

The system connects to your GitHub repository via secure token authentication. It reads the relevant source files, generates the code changes based on the audit recommendations, creates a new branch, commits the changes, and opens a pull request with a description explaining what was changed and why.

Each PR includes:

Clear title referencing the audit finding
Description of the structural issue
Explanation of the fix applied
Before state from the audit
Expected improvement after merge

The AI model chain for Score Fix runs on high-quality models with looser timeout budgets. Claude Sonnet 4.5 primary versus GPT-5 Nano. The code generation needs higher accuracy than a score generation, so the model tier reflects that.

The Credit System

Score Fix uses a credit-based system. Your $299 one-time purchase includes 250 repair credits. Each automated PR costs between 10 and 25 credits depending on the complexity of the fix.

A simple fix like adding a missing JSON-LD Organization block costs around 10 credits. A complex fix like restr

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