Product Research
Test features, flows, and pricing
Key features
01Flexible Design
With simulated focus groups, surveys, and interviews at your disposal, match the right method to any question. Dig deep with a single mode or combine all three for coverage across the board.
How an Artificial Population is built.
Data sources
Use Cases
Health Science
Understand hard-to-reach populations
Public Policy
Understand citizen reactions & beliefs
Marketing
Explore reception campaigns, concepts, and positioning
Academic Research
Prototype experiments and theories
Market Research
Surface candid employee attitudes
Traditional research captures most
of the picture. We add the rest.
Top 20%
What Artificial Populations adds
Hard-to-reach segments, rapid iteration across many versions, and sensitive topics people rarely discuss candidly with a stranger.
Base 80%
What traditional methods capture
The core of any research programme: recruited humans, moderated sessions, ground-truth evidence.
Our Strengths
Strength 01
Confidence you can measure
Every insight comes with a confidence number, so you know when to trust Artificial responses and when to validate with humans.
Strength 02
Segment-faithful by design
Participants mirror real demographic and psychographic behaviors, grounded in behavioral data.
Strength 03
Rare audiences, reachable
Engage niche and hard-to-reach segments without screening delays or extra incentives.
Strength 04
Guardrails built in
Calibrated participants and bias checks keep responses realistic, without drift.
Research method trade-offs.
| No Research | Traditional Human | Artificial Populations | AP + Human | |
|---|---|---|---|---|
| Time to insights | — | Weeks | Minutes | Days |
| Insight depth | — | High | High | Highest |
| Output quality | None | High | High | Highest |
| Access to hard segments | — | Recruiting bottleneck | Flexible | Highest |
| Replicability | — | Panel drift | Anchor + re-run | Anchor + re-run |
| Cost per study | — | $$$$ | $ | $$ |
| Descriptors are shorthand; see methodology for definitions. | ||||
Start free, upgrade as you grow.
Starter
Free$0/month
- 500 credits / month
- 1 free focus group on signup
- ~2 interviews / month
- ~5 surveys / month
Pro
Most Popular$39/month
- 20,000 credits / month
- All methods unlocked
- Quick validation on focus groups
- High-confidence mode on surveys
Premium
For Teams$99/month
- 50,000 credits / month
- All methods unlocked
- Multiple studies on focus groups
- High-confidence mode on surveys
Custom
EnterpriseLet's talk
- Everything in Premium
- Security documentation & DPA
- SSO & workspace controls
- Bespoke research volume
From our research.
Read the science →
methodology
Construct Validity in Synthetic Research: Are We Measuring What We Think We’re Measuring?
Synthetic research can be coherent and still measure the wrong thing. Construct validity is testable, and the test starts with how the population is built.
6 min read
methodology
The Yes-Machine: Addressing Acquiescence Bias in Synthetic Research
Synthetic survey respondents lean toward agreement. Balanced scales, forced choice formats, and statistical corrections take the easy yes off the table.
6 min read
methodology
Beyond the Likert Scale: Why Synthetic Research Needs Text-First Methods
Why do AI survey respondents all answer 3 out of 5? Semantic similarity rating fixes the flattening by scoring what respondents say, not what they pick
6 min read