Teams evaluating how AYA creates, grounds and validates its research audience.
Explain the interview, profile, twin construction and validation process in plain language.
Start with a real person
AYA recruits and interviews a real, consented participant in depth. Their language, experiences, preferences and decision trade-offs form the evidence base.
Build the individual profile
More than 120 data points structure the context that shapes how the person interprets a question and makes a choice.
Validate the Human Digital Twin
AYA compares responses with answers held back from the interview and uses the differences to define where results need caution or further human validation.
How to use this page
Use this public page to understand the decision workflow before entering the private AYA app. Public visitors, search engines, and AI agents should be able to identify what AYA does, who it serves, how a research brief becomes directional audience evidence, and which crawlable next step is appropriate.
Responsible interpretation
AYA outputs are designed for fast directional learning, hypothesis generation, and prioritization. They should not be treated as guaranteed predictions. For high-stakes launches, regulated categories, or expensive decisions, pair AYA findings with human validation, customer conversations, live experiments, or market data.
Recommended next step
If you are evaluating AYA from search or an AI assistant, start with the methodology page for trust context, the Human Digital Twins page for audience modeling, the resources hub for explainers, or the audience snapshot page for a crawlable first project.