Two different questions

AI visibility tells you whether you appear. Interpretation assurance tells you what was understood.

AI visibility platforms monitor prompts, mentions, citations, recommendations, and share of voice. SemanticRisk begins with the organization’s own public website evidence and records the claims AI systems extract, where models disagree, and whether those interpretations change.

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AI visibility monitoring

Observe the answer environment.

  • Track brand mentions across selected prompts
  • Measure citations, recommendations, rankings, or share of voice
  • Compare competitors and prompt categories
  • Support GEO, AEO, SEO, and marketing optimization
  • Show how often and where a brand appears
AI interpretation assurance

Examine the evidence-to-claim chain.

  • Record claims extracted from the organization’s own website
  • Compare model agreement, disagreement, omission, and scope
  • Connect interpretations to wording, structure, capture, and access
  • Separate content change, crawl regression, and interpretation drift
  • Create a reviewable history of what changed and why
Side-by-side

Different observation points produce different evidence.

QuestionAI visibilitySemanticRisk interpretation assurance
Starting pointA prompt set and generated answersPublic website evidence and controlled extraction
Primary outputMentions, citations, rankings, sentiment, recommendationsExtracted claims, classifications, model differences, evidence conditions
Main question“Do we appear?”“What was understood?”
Change over timePrompt-answer movementEvidence, capture, claim, and interpretation change
Typical actionImprove discoverability and share of voiceCorrect ambiguity, missing evidence, category drift, omission, or overstatement
Typical buyerSEO, GEO, AEO, and marketing teamsCommunications, reputation, governance, web strategy, and risk teams
SemanticRisk evidence chain

From published evidence to a reviewable interpretation record.

1. CaptureRecord accessible public website content and relevant evidence conditions.
2. ExtractIdentify the company, capability, audience, category, and positioning claims models produce.
3. CompareReview agreement, disagreement, omission, narrowing, attribution, and overstatement.
4. MonitorSeparate real interpretation change from content movement and crawl or capture regression.
When interpretation assurance matters

Use it when appearing is not the whole question.

The category is wrong

An AI system places the organization in a narrower, broader, or materially different category than intended.

Important claims disappear

Products, audiences, capabilities, ownership, geography, or differentiators are consistently omitted or narrowed.

Models disagree

Different systems produce conflicting descriptions from the same public evidence.

The description changes

The interpretation moves even though the normalized content appears unchanged.

A claim appears overstated

The generated description goes beyond what the public evidence clearly supports.

The cause is unclear

The team needs to distinguish wording and structure issues from crawl, capture, or access conditions.

For agencies

Use SemanticRisk alongside the visibility platform your agency already uses.

SemanticRisk is not positioned as a replacement for prompt monitoring, SEO intelligence, or AI share-of-voice tools. It adds an evidence-led interpretation layer that helps explain why a client may be described narrowly, inconsistently, or incorrectly—and what public evidence should be reviewed first.

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AI Interpretation Review

Turn an unclear AI description into an evidence-backed action plan.

US$299 for one company or domain. No subscription required.

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