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.
Ten weighted CITE LEDGER scoring families (avs-v3)
Schema & Structured Data — 18%
JSON-LD presence and completeness across all schema types. Checks entity references,
relationship coverage, syntax validity, and multi-type diversity. Missing JSON-LD is a
hard blocker that caps the composite score at 79.
Entity & Heading Signals — 14%
Whether the page declares a clear, unambiguous entity identity. Checks single H1
presence (hard blocker if absent), H2 section structure, title-to-OG title semantic
consistency, and entity declaration clarity.
Authority & E-E-A-T — 12%
Off-page authority signals that AI systems use to evaluate trustworthiness. Checks
Knowledge Graph entity record, SERP top-10 organic presence, featured snippet or
knowledge panel ownership, and external source citations on-page.
Meta Tags & Open Graph — 10%
Metadata completeness for AI extraction and social graph parsing. Checks title tag (hard
blocker if absent), meta description, OG title, OG description, OG image, and HTML lang
attribute.
Content Depth — 10%
Whether the page contains enough substance for AI systems to extract and cite. Checks
word count of at least 300, question-style H2 headings for snippet eligibility, and
TL;DR or summary block presence.
Crawlability & Bot Access — 8%
Whether AI crawlers can access and retrieve this page. Checks robots.txt policy, AI
crawler permissions (hard blocker if blocked), and llms.txt advisory file presence.
Renderability & Page Speed — 8%
Technical rendering signals that affect AI retrieval reliability. Checks page load under
3 seconds, LCP under 2500 ms, image presence, and image alt text coverage of at least
80%.
Citation Signal Quality — 8%
Signals that make the page easier for AI systems to quote and attribute. Checks meta
description length (25–160 chars), meta/OG description consistency, and TL;DR positioned
near the top of the page.
Indexability & Link Graph — 6%
Link graph signals that reinforce page authority and discoverability. Checks canonical
URL presence, internal links, external links, and XML sitemap accessibility.
Security & Trust Signals — 6%
Trust infrastructure that AI systems verify before citing a source. Checks HTTPS
canonical URL, language and hreflang targeting, and absence of evidence contradictions.
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.