UserCall

Teams doing qualitative research often rely on thin evidence: a handful of interviews from a single segment or shallow open-ended survey responses that don't provide enough confidence for decisions. Gathering more data means more transcripts, more manual coding, and a greater risk of losing the trail back to original quotes. Traditional interviews also require scheduling weeks in advance, are limited by researcher time and moderator availability, and are hard to replicate consistently across markets or languages. Survey answers are often vague one-liners with no context, and hand-selecting quotes from transcripts is slow and inconsistently tagged, leaving conclusions backed by disconnected or thin evidence.

UserCall runs AI-moderated, real-time voice (and text or screen-based) interviews where the AI decides whether to probe deeper, clarify, explore unexpected threads, or move on, based on a researcher-defined interview guide. It can also ingest existing transcripts, open-ended responses, recordings, and qualitative datasets and turn them into theme hierarchies and tagged excerpts, with every theme linked back to supporting quotes. Interviews can be triggered right after key customer moments like signup, onboarding, upgrade hesitation, or cancellation, and the tool integrates with analytics platforms such as Posthog, Mixpanel, Shopify, and GA. Additional features include visual mockup/prototype testing with screen recording, live interview observation, custom-branded interview links, an AI chat Q&A for querying qualitative data, and support for 25+ languages and voices, with human-in-the-loop review throughout.