Every team is curious what users think — about Fetch, about a product, a feature, an experience. Traditionally we run a survey to get that pulse. But surveys cost money, take weeks, and are rarely ready the moment we need them. And it's cumbersome to ask every user why they rated the way they did.
Can we do better with LLMs? Get the pulse in minutes — and generate the reasoning behind each rating, so we have the qualitative why alongside the quantitative what. Both, at once, on demand.
By the time results land, the roadmap moved.
For this proof we sampled real archetype-survey respondents and turned each into a persona. An LLM (GPT- or Claude-class) answers as that persona.
Built from the archetype survey: real behavior, real attitudes, their own words.
| Item | real μ | synth μ | Δ | KS |
|---|
μ is the mean rating (1–5). Δ is synthetic minus real. KS = distribution overlap: 1 − the biggest gap between the two cumulative curves, so 1.00 = identical shapes, higher is better.
Same panel, new stimulus.
So — what do our users think about this feature?
Rank candidate offers before committing partner spend.
Screen Discover / Nexus concepts before EPPO experiments — re-order the queue, don't replace it.
Pilot questionnaires on synthetic panels first; field the sharp version.
Same-day "how would your buyers react" appetizers.