This review is part of a larger series of LinkedIn newsletters titled The Human Side of AI: Cutting through the AI noise to show you how AI can be a powerful tool for your creativity, efficiency, and strategy.
Synthetic Users generates AI-simulated interview participants. You define an audience, plan a study, and the platform runs qualitative interviews against synthetic respondents built on OCEAN personality profiles, then returns an insights report with themes, verbatim quotes, and recommendations. It acts less like a chatbot and more like a research workflow that skips recruiting. The vendor is unusually direct that it is a discovery co-pilot, not a replacement for real research. That framing is the one to hold onto, because it is also where the tool earns its keep.
Key strengths
Front-loading the problem space
Before a study is scoped, a researcher can map the terrain: likely pain points, the language a segment uses, the shape of the questions worth asking. Run a problem-exploration pass, see where the interesting tensions sit, and arrive at the organic study with sharper hypotheses. Used this way it spends nothing but a few minutes and protects the recruitment budget for the interviews that actually decide something.
Stress-testing questions before you field them
This is the least contested use, and the strongest. A draft discussion guide or survey can be run through synthetic respondents to catch a leading question, a confusing concept, or an answer option nobody would pick, before a single real participant sees it. Even skeptics of synthetic research endorse guide and screener testing. It is cheap insurance against a badly built instrument.
A confidentiality posture that clears the bar
For a firm handling NDA-bound transcripts, data handling is a gating question. Synthetic Users reports SOC 2 compliance, regional EU and US infrastructure, and a Data Processing Addendum, and states it does not train shared models on customer uploads. RAG-grounded studies let a team feed its own transcripts or support tickets into the participant model without that data leaking into a common pool.
Where it could improve
The central limit is not a bug the vendor can patch. Synthetic responses are model-predicted text, not evidence of what a real person thinks or does. Two failure modes matter for B2B work. Synthetic participants tend to please, so concept tests skew positive and self-reports skew idealized. And accuracy holds up for mainstream Western audiences while degrading on the low-density populations that define B2B research: the IT director evaluating an ERP platform, the procurement lead, the niche technical buyer. Those are exactly the people a language model has seen least. Recent survey work found that while nearly all researchers now use AI somewhere, only about 8% trust synthetic participants as stand-ins for real ones. Any finding meant to survive client scrutiny should rest on real interviews. The speed argument has also narrowed as AI-assisted real-participant tools (the Anthropic Interviewer among them) have matured. Pricing adds friction too: the annual floor and demo gate put it out of reach for a solo researcher testing the waters.
Ratings
| Dimension | Rating | Rationale |
| Usability | 4.0 / 5 | The define-plan-run-report flow is fast and needs no recruiting; the main friction is commercial access, not the interface. |
| Power | 3.0 / 5 | Genuinely useful for discovery and question testing, but weak as a findings engine: sycophancy, idealization, and regression to the average. |
| Flexibility | 3.5 / 5 | Covers exploration, concept and script testing, RAG enrichment, and scaling to hundreds, but keeps you inside one simulated-respondent paradigm. |
| Cost | 2.5 / 5 | Low per-interview math against agencies, undercut by a $12,500 annual floor, token accounting, and no self-serve entry tier. |
Conclusion
Synthetic Users is a capable front end for research, not a substitute for it. It is at its best in the hours before a study exists, sharpening questions and surfacing provocations a researcher can then take to real people. Treat its output as a hypothesis to test, and the tool saves time and money. Treat it as a finding, and it will hand you clean-looking answers that the real market never gave. The judgment about which is which stays with the researcher.
Last updated: 8/6/2026.