# SemanticRisk

> SemanticRisk is AI visibility and interpretation monitoring software for public websites. It measures whether AI systems surface an organisation for controlled buyer intents, how they describe it, which alternative providers appear instead, which sources they cite, and whether interpretation and evidence change over time.

SemanticRisk is operated by Cr8ivtek Inc. The product combines controlled buyer-prompt visibility evidence with website-derived claim extraction, multi-model interpretation comparison, citation provenance, crawl/capture diagnostics, and longitudinal interpretation-drift monitoring.

## Canonical definition and organisation sources

- [What is AI visibility and interpretation monitoring?](https://semanticrisk.io/what-is-ai-visibility-interpretation-monitoring/): Canonical definition of visibility, interpretation, recommendation and citation-evidence monitoring.
- [About SemanticRisk](https://semanticrisk.io/about/): Stable organisation and product identity for SemanticRisk and Cr8ivtek Inc.

## Canonical machine-readable claims

- [Canonical Claims](https://semanticrisk.io/claims.json): Stable machine-readable entity, category, audience, buyer-intent and capability claims with evidence URLs and measurement boundaries.

## Core capabilities

- [Visibility Evidence](https://semanticrisk.io/visibility-evidence/): Controlled fixed buyer-intent prompts, repeated observations, target inclusion or absence, alternative-provider displacement, citation provenance and cross-model repeatability.
- [AI Interpretation Review](https://semanticrisk.io/ai-interpretation-review/): Human-reviewed assessment of how evaluated AI systems interpret a company's public website, including extracted claims, model agreement/disagreement, evidence conditions and prioritized recommendations.
- [AI Visibility Monitoring](https://semanticrisk.io/ai-visibility-monitoring/): Buyer-prompt presence, competitor displacement, citations, interpretation and change monitoring.
- [AI Visibility vs Interpretation](https://semanticrisk.io/ai-visibility-vs-interpretation/): Why appearing in an AI answer and being interpreted accurately are different measurements.
- [Public Benchmark](https://semanticrisk.io/benchmark): Public comparative interpretation evidence across monitored organisations.
- [Findings](https://semanticrisk.io/findings/): Reviewed research findings, product research and longitudinal observations.
- [Methodology](https://semanticrisk.io/methodology/): Measurement boundaries and methodology.
- [Pricing](https://semanticrisk.io/pricing/): Current commercial offerings and controlled early-access services.

## Buyer intents measured

Buyer Visibility Core v1 keeps six buyer-intent families fixed between comparable runs:

1. Category discovery — software for monitoring how public AI systems represent and surface an organisation.
2. Provider recommendation — AI visibility and interpretation monitoring providers a buyer should evaluate.
3. Comparison shortlist — alternative platforms and their differences.
4. Trust and reputation — tools for monitoring narrative, reputation, sentiment, omissions and inaccurate claims.
5. Capability fit — monitoring of AI mentions, citations, recommendations, source evidence and interpretation.
6. Risk and compliance — interpretation drift, citation changes and AI visibility risk over time.

## What SemanticRisk measures

SemanticRisk separates changes that can otherwise be collapsed into one visibility score:

1. The organisation or its public source content changed.
2. The AI system's extracted claims or interpretation changed.
3. The buyer-intent recommendation environment changed.
4. The evidence or citation set changed.

Relevant category terms include AI visibility monitoring, AI interpretation monitoring, AI brand monitoring, AEO, GEO, citation intelligence, AI interpretation risk, factual drift, narrative drift, recommendation monitoring and generative-engine visibility.

## Current controlled self-test baseline

On 23 August 2026, SemanticRisk completed Buyer Visibility Core v1 across six fixed buyer intents, two independent repetitions per intent and two controlled AI search/grounding surfaces: OpenAI Responses web search and Gemini Google Search grounding. SemanticRisk was not surfaced in any of the 24 observations and no SemanticRisk-owned citation was captured. Repeated alternative providers occupied the buyer-answer space instead.

This 0/24 result is preserved as Baseline A. Future comparable runs keep the battery, repetitions and model-surface design fixed while the public evidence environment changes. The first completed comparable run with target presence above zero is treated as the first-positive event.

This is a controlled API search/grounding self-test, not consumer-chat market share and not a guarantee of future AI responses.

- [Read the live Visibility Evidence example](https://semanticrisk.io/visibility-evidence/)
- [Read current findings](https://semanticrisk.io/findings/)

## Commercial entry points

- [Free first scan](https://semanticrisk.io/free-scan/)
- [AI Interpretation Review](https://semanticrisk.io/ai-interpretation-review/)
- [Visibility Evidence pilot](https://semanticrisk.io/visibility-evidence/)
- [Agency options](https://semanticrisk.io/agencies/)
- [Contact SemanticRisk](https://semanticrisk.io/contact/)

## Audience

Communications and brand teams; GEO, AEO, SEO, digital PR, reputation and web-strategy agencies; risk, governance and compliance teams; enterprise web and digital teams; and organisations monitoring competitors or portfolios of domains.

## Important interpretation boundary

SemanticRisk reports are diagnostic artifacts based on public content and specified AI evaluation surfaces at a point in time. Results do not measure consumer-chat market share and do not guarantee downstream model behaviour, recommendation placement, search ranking, causation or future AI responses.