What's the biggest gap in your current qual research workflow?
We built Mira to solve a specific problem: AI interviewers that could ask questions, but couldn't read the room.
Traditional qual research is slow. AI tools that came before could moderate — but they missed the emotional signal. A participant says "yes" while their face shows confusion. That gap is where insights get lost.
Mira detects emotion in real time via facial coding and voice AI, adapts follow-up questions based on how people actually feel, and delivers synthesis in 48 hours across 70+ languages.
We're launching on Product Hunt on July 7 and would love to hear from the research community:
👉 What's the biggest bottleneck in your current qual research workflow — recruiting, moderation, analysis, or something else entirely?
Drop your answer below — we read every response and it directly shapes what we build next.
Replies
Mira
In my experience, recruitment and moderation have become easier over time. The bigger challenge is synthesizing multiple signals—what people say, what they do, and what they actually feel—into insights that clients can confidently act on. One of the biggest gaps in research is the difference between stated responses and underlying emotional reactions. Bridging that gap is where some of the most valuable insights emerge. Looking forward to seeing how Mira addresses this.