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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.
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.
BetterClaw
This is a really interesting take on AI-powered research.👏🏻
Going beyond transcripts to capture emotions and behavioral signals could unlock much richer insights for product teams. Curious, how do you balance those advanced features with participant privacy and consent during interviews?
Decode by Entropik
@worksforme Hi Laiba — the balance is built into the session flow itself. Every participant gets a clear explanation of what will be captured before they join — webcam access for facial and eye data, microphone for voice emotion. They can decline any of these and still participate, with the system adapting to use only the available signals.
No facial recognition is used anywhere — we only measure expressions, not identity. Data is never sold or used for external training without explicit permission. Researchers control retention and deletion.
Happy to walk through the full consent flow if that would help.
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.
Timbal AI
When Mira spots a say/feel mismatch and digs deeper on its own, how do you keep that follow-up from leading the participant? A moderator reacting to visible confusion can easily plant the doubt rather than uncover it. Is the probe neutral by design, or tuned per study?
Decode by Entropik
@david_vilalta David, this is the question we lost sleep over. A leading probe is almost worse than no probe — it plants the narrative.
A few design decisions we made:
Probes are behaviorally triggered but linguistically neutral. When Mira detects a signal — a hesitation, an emotional shift, a response that doesn't match the facial/voice pattern — it doesn't say "you seemed confused." It says something like "You paused there — tell me more about what was going through your mind." The trigger is emotional, the language is open.
No interpretive language in the probe. Mira never names the emotion it detected back to the participant. It asks outward, not inward. This is a deliberate constraint built into the probing engine.
Tunable per study type, not per participant. Concept testing probes are calibrated differently from usability or brand research — because the type of signal differs. But within a study, every participant gets the same probe structure. That keeps cross-participant comparisons valid.
Probe depth is capped. Max 2 levels of follow-up on any one signal — so the interview doesn't become an interrogation.
The honest caveat: no probe is perfectly neutral. But compared to human moderators who vary tone and word choice across 40 interviews — Mira is at least consistently neutral. Happy to show you 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?
Decode by Entropik
@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
I have to confess I was worried at first because I read "interview" and assumed it was another AI recruitment solution... which opens a huge debate about ethical recruitment and the legislation around automated decision making. However, digging deeper I realise this is actually about user experience feedback... which I guess means there is no reason not to try and capture the "give aways" in terms of facial expressions and pauses etc. There has been some work done around this in the recruitment space (I remember one video platform differentiating between whether a candidate looked to the left or right when answering because it suggested whether they were using the creative or logical part of their brain... as above I think this has now been outlawed in HR processes). But in your space, I think you have the opportunity to get creative and you certainly seem to have done some great work. Best of luck to you.
Decode by Entropik
@martin_tanner Martin, this is a genuinely important distinction you have drawn, and you are right on both counts.
Facial coding in recruitment is rightly controversial and in many jurisdictions rightly restricted. The core ethical problem is using biometric signals to make high-stakes decisions about people; employment, credit, access, without their meaningful understanding or control. That is a fundamentally different situation from what we do.
In consumer and user research, participants choose to take part, they can stop at any time, and the output of the research does not affect them, it affects product decisions. The emotional signals Mira captures are used to help brands understand how people genuinely respond to products and concepts. No decision is made about them as individuals.
We also do not use facial recognition; we measure expressions, not identity. Participants are anonymous at the analysis level.
You are right that the research space is where this can be done responsibly. That is why we built here. Appreciate you thinking it through properly rather than reacting to the phrase "facial coding."