Decode by Entropik - Mira, AI moderated interviews that read how people feel
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.
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Decode by Entropik
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.
Intelli
@productrambler Congratulations Lavakumar on this launch. Mira I believe is the result of all the underground work you have been doing for the past 9 years.
How much cultural and local context have you created/designed it with i.e does it understand and differentiate accents + nuances of research candidates from Africa, Asia, Oceania etc or is it primarily designed for candidates from the Americas and Europe?
I believe the answer to the question above will be a determinant for many companies to actually trust AI to run their qual research processes.
Mira by Decode
Head of Data Science at Decode here.
The question we kept asking ourselves: how do you build an AI that understands the difference between what someone says and what they mean?
The answer is multimodal signal fusion — combining facial action units, vocal pitch, speech rate, micro-expressions, and gaze patterns into a single emotional signal per moment of the interview. Not post-hoc analysis. In real time, during the conversation.
The Voice Emotion AI specifically analyzes confidence, hesitation, excitement, and frustration from the audio layer independently of the transcript. Tone often carries a completely different story than words. That layer is invisible in every transcript-only tool.
17 patents cover the core methodologies. Happy to discuss the technical depth of any of these.
Mira by Decode
Senior Director of Consumer Insights at Decode here.
Speaking as a practitioner, the feature I find most useful day-to-day is AI highlight reels. Instead of asking a stakeholder to watch hours of recordings, Mira clips the moments that matter: where a participant's hesitation revealed unspoken doubt, where genuine excitement came through before they could temper it, where what they said and what their face showed did not match.
Those moments are what change decisions in a review meeting. And they surface in minutes, not days of manual review.
The AI Copilot is the other one worth knowing about: you can ask "which participants mentioned trust concerns?" or "show me everyone who reacted negatively to the pricing slide" across an entire study instantly.
For researchers here, what type of qualitative research do you run most? Happy to walk through how this fits your workflow.
Mira by Decode
Hi all,
Marketing lead at Decode here.
I have spent the last few months working closely with this product to build the launch. The thing that struck me most: most AI interview tools stop at the transcript. Mira treats that as the starting point.
A few capabilities people miss: AI follow-up probing that automatically asks "why?" and "tell me more" mid-interview based on what the participant actually said. AI highlight reels that automatically pull emotional moments, so you do not have to share 40-minute recordings with stakeholders. Cross-study intelligence that finds recurring themes across multiple research projects over time.
The multimodal layer — emotion AI on top of the interview — makes findings more defensible. You are not just quoting a participant. You are showing what they felt when they said it.
Happy to answer questions about the product or how we built the launch.
Mira by Decode
Product Manger at Decode here
One thing that always fascinated me about qualitative research: researchers don't just analyze what people say. They spend hours replaying interviews to understand how they said it.
A micro-expression of disgust at the pricing slide, a long pause before "I'd probably use it," hands that stopped moving the moment they said they were "comfortable."
That's the invisible layer of qual research. The part that turns a quote into an actual insight.
Mira is built around that gap, it conducts interviews in 75+ languages, probes emotionally in real time, and simultaneously captures vocal hesitation, facial emotion, and where attention went. So researchers stop losing those moments to memory and manual replay.
The goal isn't to replace researchers.
It's to give them superpowers, so they can spend more time discovering why people behave the way they do, instead of manually reviewing hours of recordings.
Happy to answer any questions about how any of this works.
How do i verify the accuracy of Facial coding and eye tracking?
Mira by Decode
@jitender_pankaj1 Our algorithms are trained and validated with millions of data points we collected over the years with our in house data collection and tagging platform. We ensure each tag has high inter rater reliability before accepting it in our dataset. Our eye tracking algorithms are frequently tested against the data from a physical eye trackers.
Onepane
congrats! user interviews are the part of building I always end up skipping because of the effort. this could actually make me do them
Mira by Decode
@ashmil_hussain Yes our entire mission behind Decode is to democratise user research. We want all the creators and builders to have data backing their creative decisions and AI guiding them to take better decisions.
Mira by Decode
Head of Sales at Decode here.
The conversation I have most with research and insights teams: "Our studies take too long, and leadership does not trust the findings."
Both problems have the same root cause. The tools being used only capture what people say, and analysis is manual. A 20-participant qual study typically takes 6-8 weeks from setup to report, arriving too late to influence the decision it was commissioned to support.
Mira compresses that timeline. Study setup with templates takes minutes. Recruitment from a 100M+ global panel is built in. Transcripts, themes, and reports are generated automatically. Emotional signal adds defensibility to findings.
If you run an insights function and want to understand what this looks like for your team's specific workflow, feel free to ask below.
Creatium
Decode by Entropik
@ridhwikvinod Ridhwik, this is exactly the right question, and honestly the one we obsessed over the most.
A few things we do to avoid over-reading:
1. Confidence thresholds, not interpolation. If a frame doesn't meet our confidence threshold (lighting, angle, partial occlusion), we drop it entirely rather than fill in the gap. We'd rather have less data than wrong data.
2. Sustained patterns, not moments. A single frown frame means nothing. We look for emotional patterns sustained across 3–5 seconds minimum before flagging them as signal.
3. Triangulation across modalities. Facial expression is one input, we cross-reference with voice tone and eye tracking. Confusion and awkwardness have different voice signatures. That cross-modal agreement is what lifts confidence.
4. Baseline calibration. We establish a neutral baseline in the first 30 seconds of every session so "this person just frowns a lot" doesn't skew the read.
The honest answer is it's not perfect, but neither is a human moderator. The difference is we surface the signal with confidence scores, so researchers can decide what to trust. Happy to walk you through exactly how this works in a live session → https://www.entropik.io/book-demo
Timbal AI
What happens when it works fine and every brand in a category runs creators against the same queries, does it become an arms race where the UGC cancels out, or is there a ceiling on how much citation share you can actually buy back?
Mira by Decode
@david_vilalta Sharp concern, and one worth taking seriously.
The short answer: the questions might converge, but the insight won't — and here's why.
The emotional layer is proprietary to each brand's product. Two competing brands can ask participants the same question about their respective checkout flows. The language of the answers might look similar. But Mira's facial coding and voice emotion data will surface where frustration spikes, which exact moment trust drops, what triggers genuine delight — and that's product-specific. You can't benchmark emotion.
Research memory compounds differently for each brand. Mira builds cross-study intelligence over time — recurring themes, longitudinal shifts, segment-level emotional patterns. A brand that's been running studies for 12 months has a fundamentally different starting point than one that just launched. That proprietary research memory is not replicable even with identical questions.
Speed becomes the moat, not secrecy. If every brand could run the same study, the winner is the one that runs it first, iterates fastest, and acts before the category moves. Mira compresses weeks of manual research into hours. That time advantage is where competitive edge lives.
The ceiling isn't on insight quality — it's on how fast you can act on it. That's what we're really building for → https://www.entropik.io/book-demo