Synthetic personas are just the start. The difference is what surrounds them — semantic scoring via embeddings, calibration and confidence scoring.
Create individual personas or entire panels matching your target audience — demographics, behavior, context. Panels can be saved and reused across studies.
Scale scores aren't numbers the model just guesses. Each persona's text answer is converted to an embedding and semantically compared against the scale's anchor statements — more stable, robust to question phrasing and free of regression to the mean.
See the full methodology →Upload real campaign results (e.g. CTR from Meta or Google Ads) or the results of an actual survey and Senthiq measures simulation accuracy against them. You see where the model fits and where it deviates.
Every insight carries a confidence score derived from answer consistency, calibration quality and domain coverage. The scoring methodology is openly documented right next to the result.
Personas react independently and in parallel — no shared answers, no herd mentality. The result is a real distribution of opinions, not one averaged voice.
Synthetic respondents tend to be overly positive and agreeable. Senthiq measures this systematic error and actively corrects it — results reflect critical voices too.
Every study ends in a clear report with methodology and confidence scores. Create a public link in one click for your team or client — no account needed to open it.
Trigger simulations straight from your own tools and pipelines. REST API with result webhooks — coming soon.
