Testing With Synthetic Users

Stop Designing Use Cases. Start Designing Capabilities.

A Practical Guide for Product & UX Teams

Product teams are under constant pressure to move faster, test earlier, and make decisions with incomplete information. Traditional user research remains essential, but it is often slow, expensive, or impossible early on. This gap has created space for a new class of tools and methods: testing with synthetic users.

Synthetic users are not a replacement for real people. Used well, they are a way to explore ideas, stress-test assumptions, and narrow down what is worth validating with real users. Used poorly, they can create false confidence and reinforce existing biases.

This guide explains what synthetic users are, when they are useful, how teams are applying them today, and how to validate their outputs responsibly.

What Are Synthetic Users?

Synthetic users are AI-generated representations of user behavior, preferences, or decision-making. They are typically produced by large language models or agent-based systems that simulate how certain user types might respond in a given context.

You will also see related terms used interchangeably:

Synthetic personas – often static or semi-static profiles used to guide simulations
Simulated users – a broader term that may include rule-based or agent-based models
Digital twins – more common in engineering and operations, sometimes adapted to user modeling

In practice, synthetic users works best as an umbrella term. It describes systems that can generate user-like responses without recruiting real people.

The key distinction is this: synthetic users do not observe the world or experience products. They infer behavior based on patterns learned from data.

Why Teams Are Turning to Synthetic User Testing

Most teams do not adopt synthetic users because they distrust real user research. They adopt them because real research does not always fit the moment.

Common drivers include:

Speed: Teams can explore ideas in hours instead of weeks
Cost: Once set up, additional simulations are inexpensive
Early-stage uncertainty: There may be no users yet, or no traffic to test against
Access gaps: Some audiences are hard to recruit or expensive to reach

Synthetic users are especially attractive when decisions are still reversible and the goal is learning, not proof.

What You Can Test With Synthetic Users (and What You Can’t)

Synthetic users are best used for directional insight, not final validation.

Common Use Cases

Early A/B exploration. Before running live experiments, teams can test variants at the pull request or design-review stage to see which direction looks more promising.
UX and copy evaluation. Synthetic users can react to flows, onboarding steps, or messaging to surface clarity issues and comprehension gaps.
Virtual surveys and feedback loops. Teams can generate large volumes of synthetic survey responses to identify likely points of friction or preference clusters.
Hypothesis generation. Rather than starting research with a blank page, teams can use simulations to generate candidate explanations for user behavior.
Expert-style critique. Some teams simulate expert reviewers to flag usability or consistency issues before human review.

Where Synthetic Users Fall Short

Measuring true emotional response
Capturing real-world constraints and trade-offs
Replacing high-stakes validation or end-to-end research

If the decision is costly or irreversible, synthetic users should not be the final input.

When Should You Use Synthetic Users?

A useful way to think about synthetic user testing is through problem clarity and decision risk.

	Low risk	High risk
High clarity	Useful for early exploration, pattern discovery, and rapid iteration.	Helpful for hypothesis generation, but real-user validation is required.
Low clarity	Can surface ideas and possibilities, but results should be treated as directional only.	Not a good fit. Simulation alone is insufficient.

Synthetic users are strongest when the cost of being wrong is low and the value of speed is high.

How to Validate Synthetic User Insights

The most important question teams ask is not “are synthetic users accurate?” but “how do we know when to trust them?”

Several validation lenses are useful:

Directional agreement. Do synthetic insights point in the same direction as real A/B tests or past research, even if the magnitude differs?
Calibration gap. How large is the difference between predicted behavior and observed outcomes, and does it stay consistent over time?
Use-case reliability. Which types of problems produce stable outputs (for example, copy clarity versus pricing sensitivity)?
Stability over time. Do results change significantly when prompts, models, or input data change?

Validation is not a one-time step. It is an ongoing process of comparison and recalibration.

Strengths and Limitations

Strengths

Fast iteration cycles
Low marginal cost
Ability to explore many scenarios quickly
Useful for narrowing the research space

Limitations

Risk of false confidence
Lack of lived experience, emotion, and consequence
Outputs reflect training data and assumptions, not reality

The most common failure mode is treating synthetic output as evidence rather than input.

The synthetic user landscape

The ecosystem around synthetic users is developing quickly. Tools generally fall into three categories:

Survey-focused tools that generate large volumes of structured responses
Behavioral simulation tools that model flows and decision paths
Interview-style tools that simulate qualitative conversations

Most teams experiment with these tools before committing them to core workflows. Maturity varies widely.

Synthetic user and automated exploration tools

Jina Synthetic Users — https://synthetic.usejina.com/
AI agents configured to explore applications and report on interactions and issues.
SyntheticUsers.com — https://www.syntheticusers.com/
A service focused on generating synthetic user behaviors for research and early-stage validation.
UXIA Synthetic Users Tools — https://www.uxia.app/blog/synthetic-users-tools
A curated overview of tools designed to simulate user interactions for usability research and testing.
testRigor — https://testrigor.com/
An AI-driven automated testing tool that explores application flows using plain-language test descriptions.
Mabl — https://www.mabl.com/
A testing platform that applies machine learning to automate and maintain end-to-end workflow checks.
testers.ai — https://testers.ai/
A service that generates and executes automated tests across web and mobile applications.
test.io — https://test.io/
A crowdsourced testing platform using human testers, often used alongside synthetic or automated tools.

Closing thoughts

Synthetic users change the economics of early research. They make it easier to ask more questions sooner and to explore ideas that would otherwise be skipped.

They do not make decisions safe on their own.

Teams that succeed with synthetic users are clear about what the method can and cannot do. They use it to think better, not to prove they are right.

Used this way, synthetic users are not a replacement for user research. They are a way to do more of it, earlier, and with more focus.