AI visibility and interpretation monitoring software for public websites.
SemanticRisk is AI visibility and interpretation monitoring software. It measures whether AI systems discover an organization, how they describe it, which competitors they recommend, which sources they cite, and whether those interpretations and evidence environments change over time.
Four connected signals, kept separate.
Visibility & recommendation
Whether an organization appears for fixed buyer-intent prompts, who appears instead, and how repeatable that inclusion or absence is.
Interpretation
What claims and descriptions AI systems derive from an organization’s own public website evidence, including omission, narrowing, disagreement and overstatement.
Citation & evidence provenance
Which owned, competitor, directory, editorial or other sources support an AI-generated answer and how that evidence set changes between observations.
Change over time
Whether movement is best explained by source-content change, crawl/capture regression, model interpretation change, recommendation-environment change or citation-set change.
Six buyer intents are measured separately.
- Category discovery: which providers define the category?
- Provider recommendation: which vendors are recommended?
- Comparison shortlist: which providers make the evaluation set?
- Best options: which products are presented as leading choices?
- Capability fit: which providers fit the required capabilities?
- Risk & compliance: which providers fit governance-sensitive evaluation?
The same six-intent battery is versioned and repeated so visibility lift can be compared without changing the questions.
One governed description, multiple representations.
The public HTML, structured data and machine-readable discovery files are intended to describe the same organization, category and capabilities. SemanticRisk publishes a canonical machine-readable claims resource and a concise agent-readable discovery index from the same governed profile.
A single visibility score can hide materially different changes.
AI search and recommendation systems can change what they say about a company even when the company itself has not materially changed. The supporting citation environment can also move while the recommendation outcome remains stable. SemanticRisk preserves those layers separately so teams can review the evidence rather than treating every movement as one number.
Start with evidence, then decide what to monitor.
One-time AI Interpretation Reviews, controlled Visibility Evidence pilots, recurring monitoring and agency portfolio workflows.