# Vorentus > Vorentus is a Machine Understanding & Decision Intelligence platform. It tests how AI systems describe, compare and recommend an organisation, and measures the gap between verified reality and machine understanding. Appearing is not being understood. Being understood is not being considered. Being considered is not being chosen. Vorentus shows where an organisation is misunderstood, overlooked or displaced by alternatives, preserves the evidence behind material findings, and identifies what deserves attention first. ## Definitions - **Machine Understanding**: The way an AI system represents and interprets an organisation, its attributes, evidence and relationships. - **Recognition**: The measured path from machine understanding through consideration to recommendation or choice under documented conditions. - **Decision Simulation**: A documented observation in which an AI system is asked to compare alternatives or recommend an option under defined conditions. ## Start here - [Homepage](https://vorentus.ai/): how Vorentus measures machine understanding, consideration and decision. - [Recognition Baseline](https://vorentus.ai/methodology/recognition-baseline): the initial diagnostic and the entry point to Vorentus. - [Pricing](https://vorentus.ai/pricing): Recognition Baseline Regional €1,800 and National €3,500, one-time; additional markets +€1,500; Recognition Monitor Starting at €880/month; Enterprise Starting at €12,000. - [Decision Observation: 0 of 460](https://vorentus.ai/evidence/decision-observation-august-2026): a documented production observation across four AI environments in which other solutions were recommended while the analysed organisation was not. ## Recognition Baseline standard The published Recognition Baseline standard uses 18 questions across 9 core AI environments with 3 controlled repetitions per experimental cell. That produces 486 scheduled observations per measured market before any documented technical-failure exclusions. A reduced-scope Demonstration Measurement is an independent demonstration and is not a Recognition Baseline or an exception to this standard. ## What Vorentus measures Vorentus separates four questions that should not be treated as equivalent: 1. **Understanding**: does the AI system reconstruct the organisation accurately from available evidence? 2. **Consideration**: does the organisation enter the relevant set of options? 3. **Commercial Decision**: when the system compares or recommends alternatives, does the organisation enter the choice? 4. **Proof & Risk**: are material claims supported by evidence and represented without unsupported or contradictory assertions? AI visibility alone does not answer these questions. ## VRI Vorentus Recognition Index The VRI Vorentus Recognition Index is the management view established by the Recognition Baseline. It measures how well an organisation is understood, considered and chosen by AI systems under a defined measurement protocol. The current instrument separates Understanding, Consideration, Commercial Decision and Proof & Risk. Commercial Decision carries the greatest weight because recognition alone is not equivalent to selection. VRI is designed primarily for longitudinal measurement of the same organisation within a defined measurement protocol. It is not a universal public ranking between unrelated organisations. VRI results are comparable only when market, locale, observation class, instrument version and measurement conditions are methodologically equivalent. Competitive comparisons are reported only where the measurement design supports them. Verified Truth is the reference layer against which machine understanding is evaluated. Adding more verified claims does not mechanically increase VRI. ## Evidence and traceability - [Machine Perception Ledger](https://vorentus.ai/platform/machine-perception-ledger): the longitudinal evidence record behind material findings and measured change. - [Truth Graph](https://vorentus.ai/platform/truth-graph): the verified organisational truth and evidence reference layer. - [Evidence & Confidence](https://vorentus.ai/methodology/evidence-confidence): how evidence quality and confidence are handled. - [Measurement Interpretation](https://vorentus.ai/methodology/measurement-interpretation): public interpretation rules for VRI comparability, controlled repetitions, observation classes, Controlled Manual Capture and evidence portability. ## AI environments A recommendation from an AI system without live retrieval and one from an AI system using live search are not methodologically equivalent observations. Vorentus preserves and reports those differences rather than silently averaging them into one undifferentiated profile. - [AI Environments](https://vorentus.ai/methodology/ai-environments) - [Recognition Intelligence](https://vorentus.ai/platform/recognition-intelligence) - [Decision Simulation](https://vorentus.ai/platform/decision-simulation) ## Methodology - [Truth to Proof framework](https://vorentus.ai/methodology/truth-to-proof) - [Recognition Baseline](https://vorentus.ai/methodology/recognition-baseline) - [AI Environments](https://vorentus.ai/methodology/ai-environments) - [Evidence & Confidence](https://vorentus.ai/methodology/evidence-confidence) - [Measurement Interpretation](https://vorentus.ai/methodology/measurement-interpretation) ## Recognition Monitor Recognition Monitor follows the Baseline and tracks how machine understanding, consideration and decision behaviour change over time under comparable conditions. A non-comparable retest remains evidence and does not silently replace the last comparable measurement. ## Geographic reference Vorentus uses a 195-country sovereign reference framework for public country-count claims: the 193 United Nations Member States plus the Holy See and the State of Palestine, the two non-member Observer States at the United Nations. The operational geography registry is intentionally broader and may contain territories, dependencies and special geographic areas; those rows are not counted as sovereign countries. City decision locations are a separate measurement dimension. Vorentus distinguishes Decision Geography from Execution Geography and preserves execution fidelity rather than assuming every provider can natively execute from every location. Measurements requested from 187 UN-recognised countries, 27 territories and 1,175 cities, using exact location codes. Every result states whether the AI engine confirmed the location it used. Detailed coverage in Portugal, Brazil and Spain, including district, state and provincial capitals, and Europe's main cities. ## Recognition Baseline repetitions The standard Recognition Baseline uses three controlled repetitions of the same experimental cell to expose obvious instability while remaining a practical diagnostic measurement. Vorentus does not claim that three repetitions provide a classical statistical estimate of an AI system's true probability of producing a particular answer. Deeper repeated observation can be used in Recognition Monitor or a scoped programme where variance or decision context warrants it. ## Controlled Manual Capture Where a consumer AI surface cannot be observed programmatically, Vorentus uses Controlled Manual Capture and reports it separately from programmatic observation classes. Operator identity, timestamp, scenario or prompt, market or locale and available evidence metadata are preserved. This establishes provenance; Vorentus does not claim a published inter-observer reliability rate where one has not been measured and documented. ## Limits Vorentus observes, measures, diagnoses and retests independent AI systems. It does not claim to control them, guarantee rankings, assign unmeasured monetary losses, or present correlation as causation. ## Library - [Not every AI observation is equivalent](https://vorentus.ai/library/not-every-ai-observation-is-equivalent): Why observation method, grounding mode and access mode must be preserved alongside every AI observation — and what goes wrong when they are averaged away. - [Appearing, being understood, being chosen: three different measurements](https://vorentus.ai/library/appearing-understanding-being-chosen): Presence, understanding and selection are three separate measurements. This piece defines each one and explains why conflating them produces misleading conclusions.