Synthetic Users simulates research participants and returns interview-shaped material: conversations you read, probe and code. If you are looking around, it is usually because the shape of the output stopped matching the decision in front of you.
It is built for the discovery end of research, where you do not yet know the question and following an answer somewhere unplanned is the whole point.
Interview-shaped output slots into an existing research practice: if your team already codes transcripts, nothing about the workflow has to change.
You define the personas, which gives you control over exactly who you are exploring with.
None of these make Synthetic Users a bad product. They are reasons a particular job stops fitting the tool.
Each option below says who it fits, what it costs you in time and effort, and where it is genuinely weak. Prices are deliberately absent: we do not publish anyone else's.
Fits when you need the unscripted insight that only comes out of a conversation with a person. Effort is substantial — recruiting, scheduling, moderating and analysing — but it is the method the whole category is imitating. Weakness: slow and expensive enough that you can only do it occasionally, and a handful of participants never represents a market.
Fits when you already have a list, a community or a support inbox. These are the people who actually buy from you, and asking them costs little more than writing the email. Effort is low, and the answers are real. Weakness: your existing customers are the people you already convinced — they will not tell you about the market that has never heard of you, and asking too often wears the list out.
Fits when you want a read across a whole market rather than a poll of N: a weighted cross-section reacts to your idea, price or campaign and you get an acceptance score plus reasoning per segment. Effort is low — write the thing you are testing, pick the market, read the report; the first check is free and needs no account. Weakness: it is a simulation. No real respondents, no statistical significance, and the panel is fixed to published population weighting, so you cannot hand-configure the audience.
Fits when the answer has to be defensible — a number for a board, a launch decision, anything you will publish. Effort is real: writing a questionnaire, sourcing a sample, fielding, cleaning and analysing, usually over days or weeks. Weakness: it is slow and costly enough that you field once and live with the questions you wrote before you knew what mattered.
Two questions that separate these cleanly.
Reynard is a simulation. It is not fieldwork, it carries no statistical significance, and it cannot replace talking to a customer. Treat it as a fast, structured second opinion that tells you where to look — not as evidence.
Both are synthetic. The comparison is about shape: interviews you read versus a weighted population read-out.
Read the comparisonThe other round-ups, plus what makes these pages different from the head-to-head comparisons.
Read the comparisonReynard publishes no accuracy figure. This page says why, and what we intend to run in public instead.
Read the comparisonOne check costs nothing and needs no account. You see the format of the output before you decide whether it is worth paying for a full run.
Start the free check