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 Citation Score Decay: Why a Page That Got Cited Last Month Might Be Ignored Today | AiVIS Cite Ledger Blogs

By R. Mason · 2026-06-05 · 8 min read · VISIBILITY-SYSTEMS

You fixed your schema, got cited, moved on. Three months later you are gone from the answer and nothing on your page changed. That is citation decay, and it is measurable.

Key Takeaways

Your deterministic CITE LEDGER score holds steady on an unchanged page; what decays is your competitive citation rate.
Citation is relative, your static page can lose citations purely from changes around it.
Decay sources: competitors adding schema, model/retrieval updates, new authoritative sources, your own entity-signal drift.
Analytics and rank do not reveal citation decay; only repeated measurement does.
The append-only Cite Ledger records when citations appear and drop; per-engine trends and drop alerts catch the slide early.
Re-audit on a schedule, watch per-engine trends, and keep entity signals (canonicals, sameAs) fresh.

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Here is a failure mode a one-time audit cannot catch: you fix your schema, you start getting cited in AI answers, you move on, and three months later you are gone from the answer, even though nothing on your page changed. Your content did not get worse. The environment around it did.

This is citation decay, and it is one of the strongest arguments for monitoring over a single snapshot.

"Score decay" vs your CITE LEDGER score, clear this up first

People say "score decay," so it is worth being precise about what actually moves. Your CITE LEDGER readiness score is deterministic: the same page evidence always produces the same score. If your page does not change, your score does not drop on its own, re-run the audit and a 74 is still a 74.

So the thing that decays is not the readiness score. It is your real-world citation rate, how often AI answers actually quote you. That number is competitive and relative: it falls when the field around you improves, even though your page and its score held steady. "Score decay" is shorthand for that slipping position, not a claim that your readiness number quietly dropped.

The signature to watch for is therefore a steady score with a falling citation rate. Your page is still sound; the bar your competitors set kept rising. That gap is the whole reason monitoring beats a one-time audit.

Why citation decays even when your page is static

Citation is relative, not absolute. You are competing for a slot in an answer that gets re-synthesized constantly. Decay comes from changes you do not control:

A competitor adds the schema you have, neutralizing your edge.
A competitor publishes a better-structured, more answer-shaped page and displaces you.
The model updates or its retrieval ranking shifts, re-weighting sources.
A new authoritative source enters the category and absorbs the citation.
Your own entity signals drift, a moved page, a broken canonical, a lapsed profile in your sameAs list.

None of these show up in y

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