DIGITAL TWINS
Builds digital twins from interviews with people matching a target customer profile. Teams can use these audiences for surveys, focus groups, concept and usability testing, and pricing exploration. Available through a company demo.
PRODUCT PROFILE
Auxos documents digital-twin audiences built from interviews with people matching a user-defined ideal customer profile. Its platform presents conversations, surveys and stimulus-based exercises for concept, usability, pricing and market-research questions.
Information from the company's product materials.
The supplier describes a research audience made up of individual twins, each based on an interview with a person who matches the target profile.
Auxos presents audience creation, chat, surveys, focus-group exploration and stimulus testing in a self-serve product interface.
Documented uses include concept and prototype testing, usability work, pricing exploration, research studies and A/B tests.
The provider directs prospective users to book a demo; it does not list public plan pricing on the reviewed page.
Methods described by the company.
Users describe an ideal customer profile in plain language using demographic, behavioral or company criteria. Auxos says it turns that description into a defined research audience.
The platform documents conversation, survey and focus-group formats alongside concept, usability, pricing, market-research and A/B test workflows.
The product examples show a population view that can be split by attributes, allowing exploration of how responses differ within a configured audience.
Data and settings the company says teams can choose.
Auxos says it finds and interviews hundreds of people who match the target customer profile. The company says each twin is based on one person from that group.
The process starts with the user's description of the audience. The result depends on how clearly that audience is defined for the question being tested.
Results reported by the company, with details about how they were tested.
The reviewed supplier page describes interview-based construction and several research formats, but does not publish a quantitative comparison of twin outputs with human responses for a named task or population.
The lack of a public task-level benchmark does not establish poor performance; it means that the page alone cannot support a numerical claim about accuracy, representativeness or commercial prediction for a proposed use.
Because the provider starts with a user-defined ideal customer profile, an overly broad, incomplete or misaligned profile can limit the relevance of the resulting research audience to the decision.
The public overview presents method and product examples, but does not provide an independent validation report, error measures or population-representativeness documentation for the displayed audience configurations.
Listing reviewed
Offers exploratory conversations with synthetic personas.
Atani.ai: offers research using AI twins instantiated from individual interview data.
Creates modeled audience participants grounded in interviews, supporting message exploration and preparation for further research.
