AIVO connects measurement, diagnosis, remediation and live answers for AI agents. Each stage tells the next where to act. Outcome tracking and repeated measurement feed what you learn into the next cycle.
AIVO Standard defines the published methodology. AIVO Win Rate tests whether AI chooses your brand. AIVO Meridian provides enterprise measurement, diagnosis and improvement. Reputation measurement examines AI’s account of your company. AIVO Assist supplies sourced product answers on your website. You can use the service that addresses your need without buying the whole system.
The published definitions and methodology behind the commercial products, including AI Win-Rate and the Linkage Gap. Methods are documented with permanent DOIs so clients can inspect how a measure is produced.
Explore the methodology →Checks whether agents can reach and read decisive information, and tests how often AI chooses your brand in repeated buying conversations. Identifies winning competitors and losing scenarios.
Feeds the next step: Access gaps, losing briefs and a baseline for later measurement.
Explore Win Rate →The enterprise measurement platform, delivered as a managed service. Maps multi-turn buying journeys, displacement points, sources and the facts AI knows but fails to use when it decides.
Feeds the next step: Decision paths, evidence gaps and priorities for diagnosis and remediation.
Discuss Meridian →Applies reputation measurement across stakeholder perspectives, including customers, investors, employees and regulators. Shows where AI’s account is thin, outdated or inconsistent with evidence.
Feeds the next step: Reputation gaps and misconceptions to investigate and address.
Explore AI reputation →Examines measurement results alongside documented facts and events to distinguish supported explanations from plausible ones. Helps identify missing evidence, stale information and other causes of lost recommendations.
Feeds the next step: Prioritized causes and the fixes that address them.
Discuss diagnosis →Addresses content, product data and technical gaps in the sources AI reads. Uses owned evidence and relevant third-party sources to strengthen the facts behind a recommendation.
Feeds the next step: Corrected, evidenced information for Assist and the next measurement.
Explore an improvement Sprint →The business’s own product expert for visiting agents and people. Checks approved, sourced facts against the customer’s need and returns fits, fits if, or no suitable option. Each answer has a reference for supported tracking.
Feeds the next step: Matched outcomes, unanswered questions and unmet needs to inform the next cycle.
Explore Assist →Begin with access and Win Rate testing. Use the findings to scope diagnosis, remediation and Assist.
Start with a Win-Rate check →Add decision measurement to see whether being mentioned leads to being chosen, and where competitors take over.
Compare the approaches →Use measurement and diagnosis to direct its work, then repeat the tests to see whether AI decisions changed.
Explore agency testing →Start with Assist to make the deciding facts available to visiting agents. Scope baseline testing and tracking during setup.
Explore the Assist launch →Begin with an AI reputation study, then investigate the causes and correct the supporting evidence.
Explore reputation research →A 30-day Sprint establishes a baseline, addresses three causes and measures again.
Explore the Sprint →A change in AI Win-Rate shows a change in tested recommendations. Answer tracking can connect an interaction to a click, basket, booking or sale when the relevant integrations and identifiers are available.
A reference code alone does not prove that an answer caused a sale. Coverage depends on the deployed tracking and the customer journey. Unmatched outcomes remain unattributed; modeled revenue exposure is reported separately from observed sales.
Questions Assist cannot answer, and needs it cannot meet, reveal where evidence or the offer needs attention. Feed those findings into the next round of measurement and improvement.