Unlike AI tools that stop at interview + transcript, Mira is a full AI researcher — plans studies, recruits globally (100M+ panel, 120 countries), runs dynamic interviews with intelligent probing, and uniquely captures what participants say AND feel via real-time facial coding, voice emotion AI, and webcam eye tracking. Extracts themes, generates insights, and produces research reports automatically. 17 patents. 70+ languages. Trusted by Unilever, Nestlé and 150+ global brands. $25M Series B.
I'm Lava, Founder of Decode by Entropik. We've been building AI that reads human behavior for 9 years and today, we're launching Mira, our AI Moderator.
Here's the problem every researcher knows but nobody talks about: people say one thing and feel another. It's called the Say-Do Gap. Self-reported data is filtered, rationalized, socially edited. Most research tools just accept this. We didn't.
Mira runs the entire research workflow, recruiting, moderating, analyzing, reporting, but uniquely captures what participants say AND feel in real time via facial coding, voice emotion AI, and eye tracking.
When someone says "I love it" but looks confused, Mira notices and probes deeper. Automatically.
Built on 17 patents. 70+ languages. Trusted by Unilever, Nestlé, and 150+ brands.
One question for the community: what's the most unreliable part of your current qual research process, and what would it take for you to actually trust AI to run it?
Drop it in the comments. I'll be here all day.
About Mira on Product Hunt
“AI moderated interviews that read how people feel”
Mira launched on Product Hunt on July 7th, 2026 and earned 189 upvotes and 104 comments, placing #5 on the daily leaderboard. Unlike AI tools that stop at interview + transcript, Mira is a full AI researcher — plans studies, recruits globally (100M+ panel, 120 countries), runs dynamic interviews with intelligent probing, and uniquely captures what participants say AND feel via real-time facial coding, voice emotion AI, and webcam eye tracking. Extracts themes, generates insights, and produces research reports automatically. 17 patents. 70+ languages. Trusted by Unilever, Nestlé and 150+ global brands. $25M Series B.
On the analytics side, Mira competes within User Experience, Analytics and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Mira performed against the three products that launched closest to it on the same day.
Who hunted Mira?
Mira was hunted by Lavakumar E. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Mira including community comment highlights and product details, visit the product overview.
Lavakumar E
Hi Product Hunt 👋
I'm Lava, Founder of Decode by Entropik. We've been building AI that reads human behavior for 9 years and today, we're launching Mira, our AI Moderator.
Here's the problem every researcher knows but nobody talks about: people say one thing and feel another. It's called the Say-Do Gap. Self-reported data is filtered, rationalized, socially edited. Most research tools just accept this. We didn't.
Mira runs the entire research workflow, recruiting, moderating, analyzing, reporting, but uniquely captures what participants say AND feel in real time via facial coding, voice emotion AI, and eye tracking.
When someone says "I love it" but looks confused, Mira notices and probes deeper. Automatically.
Built on 17 patents. 70+ languages. Trusted by Unilever, Nestlé, and 150+ brands.
First study free with code PH20 → entropik.io/platform/ai-moderator
One question for the community: what's the most unreliable part of your current qual research process, and what would it take for you to actually trust AI to run it?
Drop it in the comments. I'll be here all day.