New customers approaching the first moment of value
Ground the study in customer evidence relevant to this decision.
AI user research for growth managers
Give your next experiment a better starting point.
You know where people drop off. Explore why they might hesitate, compare the next message and focus your experiment budget on a question worth testing.
Used by
- Insights managers
- Researchers
- Product Managers
- Designers
- Copywriters
- Marketers
Trusted by
Supported by
The work in front of you
Spend traffic on sharper hypotheses.
Connect audience reactions to the question behind your next experiment. Use live behaviour to measure the impact.
- Depth interviews
Add context to a drop-off
Use depth interviews to investigate possible motivations and concerns behind a pattern in your analytics.
Explore depth interviews - UX testing
Focus the onboarding change
Examine likely UX friction around a specific task before designing the next activation experiment.
Explore ux testing - Ad testing
Pre-test the acquisition message
Explore reactions to creative and value propositions before allocating live traffic and media spend.
Explore ad testing
Your next study
What should we change in the next activation experiment?
Bring a real question. We’ll scope the audience, evidence and study together.
Start with what you have
Funnel context
Experience to review
Experiment hypothesis
From research to a decision
A stronger experiment hypothesis
Keep the question, the evidence and the next action connected.
Ask
Are trial users hesitating because the next step feels unclear or too demanding?
Examine
Explore the language and expectations surrounding that step. Use the research to distinguish plausible explanations for the observed drop-off.
Act
Choose a focused change and measure its effect on activation in a controlled live experiment.
Illustrative research brief. This is an example of how to use findings, not a completed customer study or a product screenshot.
Internal back-testing
UX testing. Measured against a human reference.
Sunzuin minutes75%
Human expert with AIin days34%
Research, 500+ humansin months100%
Sunzu internal back-testing. Research, 500+ humans is the reference set to 100%. This is a study-specific comparison, not an overall accuracy score or a guarantee for your study.
From our customers
Relied on by global experts
Product Manager
This is really remarkable stuff. I was just genuinely blown away when I saw the quality of the usability results that came back. I also really liked the humanizing of it and particularly the level of introspection the characters had.
Director, Consumer Insights & Strategy
In just a couple of months, over 170 of our colleagues have run over 500 studies.
We’re now directly integrating synthetic audiences into many of our research, product and marketing workflows.
Director, Global Product and Design UX Research
In a market flooded with AI-generated noise, this was the first platform that genuinely earned our trust.
By grounding its models in research principles, behavioural science, and real customer data, it delivers insights that feel credible, transparent, and actionable.
It changed our perspective on what’s possible with AI in customer research.
A few useful answers
Before your first study.
No. It helps you develop and narrow hypotheses before a live test. Your experiments and analytics measure actual conversion, retention and revenue effects.
Product analytics are one form of customer evidence that can help ground an audience, alongside research, surveys, CRM data and support themes. Discuss the relevant sources for your use case.
Sunzu simulates human behaviour with AI, grounded in customer evidence and enriched with a behavioural corpus. Its synthetic humans are simulations. Use findings to explore directions and sharpen questions; lived experience, unexpected behaviour and consequential decisions still need research with real people.