Synthetic Users

User research is slow and expensive: recruiting the right participants, scheduling interviews and synthesising findings can take weeks. Synthetic Users offers a radical shortcut, interviewing AI-simulated participants instead of people.

You define who you want to hear from, such as a procurement officer, a clinician or a retail manager, and run interviews with synthetic respondents in minutes. It sits among research and go-to-market tools like Clay, but it addresses a different question: what might these people think, before you ask them for real.

This review covers what Synthetic Users actually does, the evidence it cites, who it fits, and why the validity line matters.

Synthetic Users Simulated-Research Facts

AI 'Participants' For Research, With A Validity Line

Synthetic Users replaces recruited participants with AI respondents built to represent specific roles and contexts, so a product team can 'interview' a paediatric oncologist or a port logistics planner in minutes. For early exploration, that speed and low cost are genuinely appealing.

The idea is not as far-fetched as it sounds. Large language models encode a great deal about how different professions talk and what they worry about, so simulated interviews can surface plausible themes, objections and vocabulary quickly.

But there is a hard validity line. A synthetic respondent is a model's prediction of what such a person might say, not a person with real experiences, constraints and contradictions. It can echo stereotypes, sound more articulate and agreeable than real people, and miss the unexpected behaviour that makes real research valuable.

To its credit, Synthetic Users itself says it is a discovery co-pilot, not a replacement for real research, designed to front-load the problem space and help you spend your real research budget more wisely. That is exactly how it should be used, and how we score it.

A synthetic respondent is a model's prediction of what a person might say, not a person; use it to prepare for real research, not to replace it.

What Does Synthetic Users Simulate, Exactly?

The platform produces simulated interviews and points to evidence that they resemble real ones.

Simulated Respondents And Fast Interviews

You describe the audience and research goals, and Synthetic Users generates respondents and runs interviews, returning transcripts and synthesis without recruitment fees or scheduling. That makes it fast to test several audiences, compare segments or pressure-test an interview guide.

For grounding in what real customers already say in public, a monitoring tool like BrandMentions surfaces genuine conversations, a useful real-world check against simulated opinions.

The Evidence, And Why It Is A Co-Pilot Not A Replacement

Synthetic Users cites comparison studies reporting substantial overlap between synthetic and real interview themes, a quote that AI feedback matched human feedback most of the time, and a list of published work supporting the approach. These are vendor-cited, and 'thematic overlap' is not the same as predicting what real people will actually do or buy.

If you want to read the underlying research yourself, a literature search tool like Consensus can help you find independent studies on synthetic respondents and LLM-simulated surveys, including their known biases.

Who Synthetic Users Fits, And Who It Does Not

Synthetic Users fits product managers, UX researchers, founders and strategists who want to explore a problem space fast, draft and refine interview guides, generate hypotheses, or prepare before talking to hard-to-reach professionals.

It suits teams with limited research budgets who want to spend real interviews more wisely, and researchers who use synthetic runs to spot gaps in their questions before fieldwork.

It does not fit decisions that must rest on real user evidence, such as launches, pricing, regulated or high-stakes products, or research on under-represented groups where models may encode bias. Treat its parity figures and testimonials as vendor-cited claims.

What Synthetic Users Costs Per Interview

Synthetic Users describes per-interview pricing far below traditional agency research, with demos and self-serve options. We do not publish prices, which change; check the site.

The value question is not just cost per interview but what the interviews are worth: cheap synthetic answers are valuable for exploration and preparation, and misleading if treated as evidence.

The sensible approach is to budget it as a pre-research tool, use synthetic runs to sharpen questions and hypotheses, then spend real money on real participants to validate what matters.

A Sceptical First Study With Synthetic Users

Because the risk is mistaking simulation for evidence, trial Synthetic Users against research you have already done with real people.

1. Pick a study you already ran with real participants.
2. Recreate the audience and questions in Synthetic Users.
3. Compare themes, objections and surprises with your real findings.
4. Note what the synthetic run missed or made more agreeable.
5. Use it to refine your next interview guide, not to replace the interviews.
6. Pass/fail: it is worth it if it sharpens real research, not if it replaces it.
7. Treat parity figures and quotes as vendor-cited claims.

Synthetic Users, The Honest Brief

• Synthetic Users simulates research participants: AI personas you can interview in minutes instead of recruiting people.
• It is fast and cheap for early discovery: map a problem space and sharpen questions before real research.
• Simulated is not real: AI respondents can reflect stereotypes and miss the surprises real users bring.
• The vendor agrees: it calls itself a discovery co-pilot, not a replacement, which is the right way to use it.

Terms For Judging Synthetic Research

Synthetic respondent

An AI-simulated research participant designed to represent a role or audience, rather than a real person.

Organic research

Research with real human participants, such as interviews, surveys or usability tests.

Synthetic-organic parity

How closely synthetic interview results match real ones, usually measured on themes rather than actual behaviour.

Discovery

The early research stage of exploring a problem space and forming hypotheses before validating them.

Is Synthetic Users Right For Your Discovery?

Product Teams Synthetic Users Can Help

• Product teams exploring a new problem space
• Researchers refining interview guides
• Founders forming early hypotheses
• Teams preparing for hard-to-reach professionals
• Budget-limited teams planning real research

When Synthetic Users Misleads You

• Launch, pricing or go/no-go decisions
• Regulated or high-stakes product research
• Research on under-represented groups
• Anyone treating synthetic answers as evidence
• Teams skipping real research to save money