AI search visibility audits

Understand how AI search represents your brand.

CiteFrame evaluates AI-generated answers, source evidence and technical conditions, then delivers prioritized findings and implementation guidance.

An audit service for marketing, SEO and growth teams.

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The audit framework

Six layers.
A complete view.

Distinct evaluation layers for technical conditions, source evidence and model responses. Findings follow the agreed scope and available evidence; cross-cutting metrics remain separate.

Technical ingestion

Synthetic crawler responses, access policies, retrieval and machine-readable delivery.

Context / Knowledge Graph

Structured entity context, schema relationships and content extractability.

Citation / search evidence

Native citations, search-grounded source observations and their provenance, where exposed.

Content alignment

Sequence-based lexical overlap between model answers and selected captured source text.

Factual representation

Claim-level verification against available source evidence, with support and coverage kept separate.

Downstream actionability

Whether available observations support validated next-step conclusions.

Client deliverables

Clear outputs for your next steps.

Availability and depth depend on the agreed scope.

Executive GEO summary
Measured results, evidence states and priorities for decision-makers.
Sample PDF (new tab) ↗
Evidence bundle
Where included: prompts, responses, source captures and provenance for reviewing the findings.
Technical findings and implementation artifacts
Documented gaps and proposed files. Implementation starting points require target-framework and deployment validation.
Action & Implementation Brief
Prioritized remediation and technical guidance for your team.
Sample PDF (new tab) ↗

Measurement approach

How the measurements are interpreted.

AI visibility, source alignment and factual accuracy answer different questions. CiteFrame keeps these signals separate, retaining the prompts, responses, execution outcomes and available source evidence behind each observation.

Share of Model Voice (SoMV)

SoMV summarizes how the brand appears across the evaluated prompts and AI engines under a defined scoring policy. Mentions, citations and recommendations are interpreted in query context.

Sample SoMV
18 / 100

Key limitationCiteFrame’s own named metric describes the tested sample, not universal market share or guaranteed future visibility.

ROUGE-L / content alignment

ROUGE-L measures sequence-based lexical overlap between sampled model responses and selected source material. CiteFrame uses it to identify wording and content mismatches.

Sample average ROUGE-L · FAIL
0.0302

Key limitationNeither a semantic-equivalence test nor a factuality score. Low overlap alone does not establish a false claim.

Claim-level factual verification

Individual claims are extracted and assessed against available site evidence. Supported, contradicted, unsupported and unverifiable are separate outcomes. Factual fidelity concerns supported decisive claims; verification coverage describes the proportion of extracted claims assessed decisively.

Decisive claims supported · 36.8% coverage
0 / 7

Key limitationUnsupported does not mean contradicted. Only 7 of 19 extracted claims were decisive in this sample.

Citation and source evidence

The audit retains native citations and source observations when exposed by the AI provider, allowing source attribution and provenance to be reviewed.

Sample citation / search evidence
UNMEASURED

Key limitationWhen sufficient evidence is not available, the measurement stays UNMEASURED rather than being scored as zero. UNAVAILABLE is a separate evidence state.

Technical accessibility

The technical layer checks crawler-like responses, access directives, structured data and machine-readable retrieval. These observations inform technical findings within the applied audit policy.

Synthetic tests · browser control + five crawler identities
6 / 6

Key limitationSynthetic tests use controlled request conditions. They do not prove access, indexing or use by production AI systems.

Competitor surfacing was UNAVAILABLE in the sample: no validated comparative evidence. Missing evidence remains explicit; no blended score replaces the six layer states.

Research context

Research informs the questions.
Evidence grounds the findings.

These works provide research context for generative search visibility, textual overlap and atomic factual evaluation. CiteFrame uses its own audit implementation, execution contracts and evidence policies; it does not reproduce these papers’ exact experimental methodologies.

An audit partner for your client work.

For SEO, content and growth agencies that need evidence to guide an AI search engagement. Discuss the client’s questions, agree on the audit scope and use the findings to shape your implementation work.

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Request an audit

Request an AI search visibility audit.

Share your website, the questions you need answered and any relevant constraints. We’ll discuss scope, deliverables and pricing.

Choose a request method

Email an audit request

Opens your email app. You review and send the inquiry.

Request through Upwork (new tab)

For teams that prefer to work through Upwork.

CiteFrame is provided by Ignacio Rotth.