The future of research is hybrid
The future of research is hybrid
AI participants for speed, human research for depth. Validate earlier, iterate more often, and decide with more confidence.
THE PROBLEM
Research is too slow for modern product cycles.
Research is too slow for modern product cycles.
Teams now ship weekly, sometimes daily. But recruiting participants, scheduling sessions, moderating interviews, and synthesizing results still take days or weeks.
When research can't keep up, teams don't wait. They skip validation and find the friction after real users hit it. The problem was never that teams stopped caring about users. It's that the method stopped fitting the pace.
Teams now ship weekly, sometimes daily. But recruiting participants, scheduling sessions, moderating interviews, and synthesizing results still take days or weeks.
When research can't keep up, teams don't wait. They skip validation and find the friction after real users hit it. The problem was never that teams stopped caring about users. It's that the method stopped fitting the pace.
OUR APPROACH
AI for speed, humans for depth
AI for speed, humans for depth
The space is new and everyone is still improvising. Every team measures differently, so results from one study rarely match the next. Consistency is shaky even with real people: studies show the same test run two weeks later with the same participants already differs by around 20%. On top of that, even the well-known accuracy figures come with heavy caveats. Without a shared method, it's hard to know when a synthetic result actually deserves your trust.
The space is new and everyone is still improvising. Every team measures differently, so results from one study rarely match the next. Consistency is shaky even with real people: studies show the same test run two weeks later with the same participants already differs by around 20%. On top of that, even the well-known accuracy figures come with heavy caveats. Without a shared method, it's hard to know when a synthetic result actually deserves your trust.
AI RESEARCH
HUMAN RESEARCH
Speed
Minutes
Days or weeks
Best for
Early-stage flows
High-stakes decisions
Cadence
Repeated iteration
Deep context
Strength
Friction detection
Motivation & nuance
Outcome
Hypothesis generation
Real-world validation
OUR BELIEFS
Validation should keep pace with the roadmap
Validation should keep pace with the roadmap
If a test round costs weeks, teams ration research. If it costs minutes, they run it constantly.
If a test round costs weeks, teams ration research. If it costs minutes, they run it constantly.
We won't ask you to trust synthetic results on faith. We earn it the slow way — small experiments, real predictions, and checking whether they held up against actual users.
We won't ask you to trust synthetic results on faith. We earn it the slow way — small experiments, real predictions, and checking whether they held up against actual users.
AI participants extend research; they don't replace it
AI participants extend research; they don't replace it
Nothing replaces watching a real person fumble through a flow. AI participants replace the waiting.
Nothing replaces watching a real person fumble through a flow. AI participants replace the waiting.
We believe the safest place to start is stress-testing a study or design before any human sees it. Worst case, we surface a weakness you'd have missed — there's no downside.
We believe the safest place to start is stress-testing a study or design before any human sees it. Worst case, we surface a weakness you'd have missed — there's no downside.
Prompt-only personas have no place in a product decision
Prompt-only personas have no place in a product decision
A statistical impression of a demographic an LLM read about online isn't a participant. Behavior on your real product is.
A statistical impression of a demographic an LLM read about online isn't a participant. Behavior on your real product is.
We refuse to build on prompts alone. This space has no settled method yet, and results only get shakier when you work backward from a pile of old transcripts, so we ground every participant in real product behavior.
We refuse to build on prompts alone. This space has no settled method yet, and results only get shakier when you work backward from a pile of old transcripts, so we ground every participant in real product behavior.
Honesty about limits is part of the method
Honesty about limits is part of the method
A method that hides where it's weak can't be trusted where it's strong. Knowing when not to rely on AI participants is as important as knowing when to.
A method that hides where it's weak can't be trusted where it's strong. Knowing when not to rely on AI participants is as important as knowing when to.
We'll always tell you where synthetic breaks. It can point to whether users prefer A or B, but not reliably how much — and it's sharper on logical calls than emotional ones. "Can we raise prices?" is a useful read; "by $5 or $10?" isn't.
We'll always tell you where synthetic breaks. It can point to whether users prefer A or B, but not reliably how much — and it's sharper on logical calls than emotional ones. "Can we raise prices?" is a useful read; "by $5 or $10?" isn't.