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.

AiVIS Cite Ledger Starter Tier, Evidence-Backed AI Audits from $15 | AiVIS Cite Ledger Blogs

By R. Mason · 2026-04-10 · 6 min read · STRATEGY

Starter is where AI visibility stops being guesswork: full evidence manifests, implementation-grade outputs, and repeatable fix workflows.

Key Takeaways

Starter tier bridges the gap between free Observer and $49 Alignment with 30 scans/month at $15.
Uses the same paid AI model (GPT-5 Nano) as Alignment, no free-tier rate limits or truncation.
Includes full recommendations with implementation code, PDF export, shareable links, and force-refresh.
Does not include competitor tracking, citation testing, evidence clusters, or triple-check pipeline.
Annual billing available at $140/year.

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Why We Built the Starter Tier

When we launched <a href="/pricing">AiVIS Cite Ledger pricing</a>, the jump from Observer (free, 3 scans/month) to Alignment ($49/month) left a gap. Freelancers running a handful of client sites, solo founders validating product pages, and content creators optimising a personal brand all needed more than 3 scans, but not 100. They needed a paid AI model, not the free-tier fallback. And they needed the full fix plan, not a truncated summary.

Starter fills that gap.

What Starter Includes

**30 scans per month**, enough for a small portfolio of pages, weekly re-audits of key landing pages, or a focused sprint on a single domain.

**Paid AI model (GPT-5 Nano)**, the same model that powers <a href="/pricing">Alignment</a> audits. No free-tier rate limits, no reasoning-token overhead, no truncated responses. Every scan returns the same depth of structural analysis that paying customers get.

**All recommendations with implementation code**, Observer shows you what is wrong. Starter shows you what is wrong and gives you the code to fix it. Every recommendation includes copy-pasteable implementation snippets: <a href="/signals/json-ld">JSON-LD blocks</a>, meta tag corrections, heading restructuring patterns, and robots.txt directives.

**Content highlights**, the key structural signals the AI model identified as strengths or weaknesses, surfaced as a scannable summary before the full recommendation list.

**PDF export**, generate a branded report you can send to a client, attach to a proposal, or archive for your records. The export includes the full score breakdown, all recommendations, and implementation code.

**Shareable links**, create a public link to any audit result. Share it with a teammate, client, or stakeholder without requiring them to log in. Starter share links are not redacted, the recipient sees the full result.

**Force-refresh**, bypass the cache and re-run the audit with fresh data. Useful after you have implemented

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