Buyers, researchers and procurement teams assessing AYA evidence, security and responsible-use boundaries.
Explain how AYA is validated, what the alignment result means, what AYA will not predict and how customer research is protected.
Qualified validation evidence
Across AYA validation testing, Human Digital Twin responses show 87% average alignment with the people they represent on themes, preferences and reasoning. This is not word-for-word identity or a commercial forecast.
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Private workspaces
Other customers cannot access a workspace’s questions, uploads, studies or results. AYA does not sell customer research or disclose it to competitors.
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.