Find failed experiences before users do.
A synthetic user is an AI that uses your product like a real person — it opens your site in a real browser, works through your flows, and reports back where it got confused, lost trust, or gave up.
Run them across your onboarding, signup, and checkout before real customers hit them — and find out what converts without waiting for scale.
SyntheticUsers discovers WHY users leave
You can't tell if it works until it's too late
We can build software faster than ever — but the feedback loop on whether it actually works for a person hasn't caught up.
How do you know the homepage you just shipped converts? You can't A/B test it until you have thousands of users hitting it. User research is slow. Analytics shows where people dropped off, not why. So you ship, and you wait, and you find out a flow is broken after someone's already gone.
Synthetic users tell you whether people can get through your product — and whether it converts — before you have the traffic to find out the hard way.
Catch usability failures before meaningful traffic exists.
Quick checks while real user research stays precious.
Rerun critical flows when onboarding, pricing, agents, checkout, or activation changes.
Paste a product link
Works with preview links, prototypes, staging apps, and live products.
Preview links, prototypes, staging apps, and live products.
Pick a persona and the journey they're trying to complete.
They open a real browser and work through the flow like a person.
Where they got confused, lost trust, or gave up — with evidence.
Ship the fix, run it again, and watch the verdict change.
The moments a flow quietly loses someone
Synthetic users surface the human failure modes analytics can't name — each reported in the persona's own words.
"I don't know what to do next."
"I'm not sure this actually happened."
"This feels like too much work."
"I tried, but never got what I came for."
"The product used different words than I expected."
"The system worked, but I didn't know it."
A usability report developers can act on
Not a score — a verdict, the failure moment, the evidence in the persona's own words, and the fix. Switch personas to see the same flow through different eyes.
Green evals, confused users
Evals test whether a model gave the right answer. Synthetic users test whether someone can complete the product journey. Your evals can pass while users still get confused, lose trust, or abandon the task.
Your evals can be green while your product is quietly burning its first relationships.
I built an AI-first chat app and gave it to a real customer to test. It passed evals — but the behavior wasn't what I expected, and it burned that first relationship. That's exactly what I should have caught with simulated users first.
Is this actually working for the person on the other end?
It's the same question at every level of what you build. Today that's your website and product. Next it's your AI experiences. Further out it's your multi-agent systems — and you can't A/B-test your way to those answers without scale you don't have yet.
We're building toward that as a full developer platform for product behavior:
Run synthetic users on every push, deploy, and preview.
Let your own agents run the tests and iterate on copy, design, and layout.
Map every path through your product and test each.
Email today; Slack, GitHub, and chat next, so agents full-cycle simulate real usage.
An always-on synthetic user you can talk to.
Build synthetic users into your own CI and test suites.