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, See If AI Actually Cites Your Site

AiVIS Cite Ledger is an evidence-backed AI citation readiness audit. It measures how ChatGPT, Perplexity, Claude, and Google AI interpret, trust, and cite a website, then returns a 0–100 CITE LEDGER score with BRAG evidence-linked findings and prioritized fixes.

The platform is not a traffic dashboard. It is a deterministic evidence pipeline that captures crawlable page signals, evaluates answer-engine citation behavior, commits citation records to an immutable ledger, and derives registry scores from committed evidence.

Gap: AI answer engines need extractable citation evidence

Traditional search rank does not prove answer-engine inclusion

A website can rank in traditional search while still failing AI citation readiness. Answer engines need clear entity definitions, structured data, crawlable text, canonical URLs, source trust signals, and answer-ready sections that make attribution unambiguous.

Unstructured pages create citation gaps

Common blockers include missing Organization or WebPage schema, weak FAQ coverage, vague product definitions, thin body copy, duplicate or absent canonical tags, broken heading hierarchy, inaccessible robots or sitemap signals, and absent external identity references.

Uncited claims are treated as evidence gaps

AiVIS Cite Ledger labels unsupported AI claims as uncited instead of converting them into fabricated authority claims. The system downgrades uncited output, exposes the gap in the report, and ties the recommended fix to the specific missing evidence.

Evidence: the CITE LEDGER records what was found

Each audit starts with live extraction

The scan captures HTML, metadata, JSON-LD schema, headings, internal links, canonical tags, content depth, policy signals, and crawlability evidence from the submitted URL. Extracted evidence becomes the source material for scoring and remediation.

Parallel signal checks test citation behavior

The signal layer evaluates AI-model interpretation, web-search corroboration, instant-answer signals, mention sources, and SERP evidence where the workspace tier allows it. These checks identify whether the entity is present, absent, displaced, or cited without enough support.

Ledger rows are the source of truth

Committed CITE LEDGER records preserve the query, model or source context, URL evidence, citation state, and hash-locked traceability root. Registry metrics such as visibility score, authority score, entity clarity, and query coverage are derived from those committed rows.

Fix: recommendations are ranked by citation impact

Schema fixes improve machine-readable identity

AiVIS prioritizes Organization, WebSite, WebPage, SoftwareApplication, FAQPage, DefinedTerm, BreadcrumbList, and product or service schema when those structures match the page intent. The goal is entity clarity, not schema stuffing.

Content fixes make answers easier to extract

Recommended content changes focus on answer-first definitions, short evidence-backed explanations, gap-evidence-fix sections, product boundaries, tier language, methodology summaries, and FAQ answers that align with the visible page and JSON-LD.

Technical fixes protect attribution

Canonical URLs, HTTPS, semantic landmarks, image alt text, internal links, robots policy, llms.txt guidance, sitemap freshness, and policy or contact pages help crawlers verify the page and connect the entity to the correct publisher.

Seven weighted CITE LEDGER dimensions

Schema and structured data

This dimension checks JSON-LD coverage, schema type fit, entity graph consistency, sameAs identity links, FAQ availability, and validation quality.

Content depth

This dimension checks whether the page has enough crawlable, specific, answer-ready text to support a citation rather than a vague mention.

Technical trust

This dimension checks HTTPS, canonical presence, internal links, response performance, semantic landmarks, and accessibility cues such as image alt coverage.

Meta tags and Open Graph

This dimension checks title length, meta description quality, Open Graph alignment, social preview clarity, and attribution consistency.

AI readability

This dimension checks whether schema, headings, FAQ entities, and answer-style copy align so models can parse the page without guessing.

Heading structure

This dimension checks for one descriptive H1, useful H2 sections, granular H3 subsections, and a semantic outline that maps the page intent.

Security and trust signals

This dimension checks HTTPS, sameAs links, author or publisher signals, policy links, contact signals, and other cues that support source trust.

Methodology summary

Input validation and extraction

Every analysis begins with a safe URL, sanitized user-supplied fields, and schema validation before the page is fetched and parsed. The extraction step builds the entity graph used by later checks.

Citation resolution gate

AI claims must be matched to real page evidence, web results, or SERP signals. If support is missing, the claim is labeled uncited and surfaced as a gap rather than treated as verified authority.

Immutable ledger and registry derivation

The ledger commit creates stable scan traceability. Registry values are read-only aggregates computed from ledger rows, which keeps public reports reproducible and prevents client-authored score overrides.

Actor boundaries and audit scale

Platform owner controls stay internal

The platform owner operates infrastructure, policy, orchestration, billing integration, and system-level controls. Those controls are not described as workspace-member capabilities because customer-facing pages must distinguish operator responsibilities from workspace features.

Workspace members receive evidence views and actions

A workspace member submits URLs, reviews citation readiness reports, exports evidence, compares gaps, and applies remediation guidance. The member sees the scan result, live execution state, and evidence-backed output rather than an invented persistent dashboard metric.

Agent runtime performs background execution

Automated workers, schedulers, queues, and browser execution loops perform extraction, probing, visibility observation, drift checks, and publication tasks. Runtime internals are surfaced only as trace metadata where that information helps verify the scan.

Large audits separate discovery, sampling, and deep analysis

For large websites, AiVIS separates broad URL discovery from deterministic sampling and expensive deep analysis. Public telemetry should distinguish URLs discovered, pages sampled, pages fully audited, sampling strategy, and coverage so a cap is never mistaken for total site size.

Canonical access tiers

Observer and Starter

Observer provides entry-level citation readiness visibility. Starter adds more evidence detail and practical fixes for smaller teams that need the reasons behind the score.

Alignment and Signal

Alignment supports deeper diagnostics, competitor context, and recurring evidence review. Signal adds advanced validation, monitoring depth, and multi-model verification workflows.

Agency and Score Fix

Agency supports portfolio-scale workspace operations. Score Fix is a one-time remediation workflow for evidence-linked implementation changes.

Frequently asked questions

What is AiVIS Cite Ledger?

AiVIS Cite Ledger is an AI visibility and citation readiness engine that measures whether answer engines can parse, trust, and cite a web entity with traceable evidence.

What is BRAG?

BRAG means Based-Retrieval-Auditable-Grading. It is the evidence gate that connects each finding to observable page or citation evidence and marks unsupported claims as unknown or uncited.

What does a public report contain?

A public report contains the CITE LEDGER score, platform score breakdown, key findings, prioritized recommendations, and an evidence snapshot derived from the scan record.

How should improvements be verified?

After implementing fixes, the same URL should be re-audited so new ledger rows can verify score movement, resolved blockers, and remaining citation gaps. A re-audit should confirm that the corrected page still serves the same canonical URL, includes the intended schema graph, preserves the visible answer blocks, and exposes enough policy and publisher evidence for citation engines to attribute the source without ambiguity. This closes the evidence loop from gap to fix to verified citation-readiness movement.