Panelia vs Synthetic Users: qualitative interviews or quantitative measurement

There are already other tools out there that simulate respondents with AI, and it is tempting to lump them all together. But Synthetic Users and Panelia answer a different question: one gets an individual talking at length, the other measures a trend across a sample.

What Synthetic Users does

Synthetic Users is a startup that generates AI-driven synthetic participants, using a multi-agent architecture combining LLMs and retrieval (RAG), for fast qualitative research: problem exploration, concept testing, scripted interviews. Billing is per interview, positioning skews agency and enterprise, with SOC 2 compliance, and offices in the United States (Los Angeles), Portugal (Lisbon) and the United Kingdom (London). The tool is built to recreate the experience of a one-on-one qualitative interview: you ask open questions to a synthetic persona and explore its answers the way you would with a real interview participant.

What Panelia does

Panelia does not simulate an individual conversation, it measures a distribution. You describe a concept, a price or a message, and within minutes you get a purchase intent split across hundreds of synthetic respondents, with a confidence interval, based on a method published on arXiv (2510.08338) and calibrated on real human data. Expect roughly 1 euro and 10 minutes. The output is not an interview transcript, it is a statistical figure paired with illustrative verbatims and a summary.

Two formats, two jobs

If your question is "why does this problem exist" or "how does this user experience this situation", a qualitative interview stays the right format: probing, following up, chasing an unexpected line of thought is exactly what a conversation, synthetic or not, is good for. Synthetic Users built its product around that use case. If your question is closer to "what share of my market would buy at this price" or "which message converts best across three versions", you need a measurement across a sample, not even a very thorough individual conversation. That is what Panelia is built for.

A shared limit worth naming

Both tools run on language models, not on recruited real people. That is a genuine speed and cost advantage, but also an honest limit to keep in mind: a synthetic persona, however well calibrated, remains a simulation. Panelia is upfront about this: it is a decision-support tool, calibrated on real data to stay close to observed behavior, but it does not replace a human test when the stakes are genuinely critical.

A concrete example

Take a simple case: you want to understand why users abandon your checkout, then decide what price to relaunch an offer at. For the first question, an in-depth qualitative interview, whether through Synthetic Users or with real users, lets you dig into context, hesitations, comparisons made with other solutions. For the second question, even a rich interview is not enough: you need to know what share of the market would buy at 19, 29 or 39 euros, with an associated margin of error. That is where Panelia takes over, with a numeric distribution rather than an individual impression, however well argued. The two steps complement each other, and are rarely interchangeable.

Doing both, in the right order

In a product discovery cycle, the two approaches build on each other more than they compete. A handful of qualitative interviews, synthetic or real, helps you understand motivations and shape good hypotheses about a concept, price or message. A quantitative measurement like Panelia's then lets you decide between those hypotheses with a number, not an impression. Using one to explore and the other to measure avoids the classic mistake of sizing an entire market off three conversations, however rich they were.