Mira - AI moderated interviews that read how people feel

@doanj7hh "Flagged where respondents actually felt something" is the signal we built toward — not a wall of quotes, but the specific moments that matter emotionally. Glad the insight pull was faster than expected.

@duyguridapagq Our emotion AI is trained to understand the demographic difference while predicting emotions using the face. Later we do a few normalisations based on the benchmarks we have collected to give you unbiased scores and emotion data points. We just need permission to use the microphone and the webcam.

@elanurybrp This is exactly the feedback that matters most, thank you, Elanur.

The hesitation flag mid-interview is one of those moments that traditional transcripts completely lose. You see it in real time, the participant moves on, and by the time you are reviewing notes it is gone.

Would love to hear more about what you were testing, if you want to run a more structured study, first one is free this month. https://aimoderator.entropik.io

@krakl9tl6 "Useful rather than just a novelty" is exactly the bar we hold ourselves to. A lot of emotion AI tools add a layer but do not meaningfully connect it back to the insight. Glad the report turnaround and emotional signals both landed as useful for your concept test.

@sleymanpelv3z3 "Summaries vs actually digging into how people feel" is a real distinction. Most tools stop at summarising what was said. The signal that actually drives behaviour lives in a different layer. That is what Mira tries to preserve.

@medal411 "Even directionally right" is an honest way to frame it, and that is genuinely where the value sits for a solo founder. You are not running a clinical study. You are trying to know whether the hesitation you sensed in three conversations is real or in your head.

Mira gives you a signal to pressure-test that instinct, faster than you could manually review recordings. First study is free this month if you want to try it on something live.

@poyraz853752 The recruiting + synthesis in one pass is one of the things we are most proud of — most tools make you stitch together three or four platforms to get from question to insight. The hesitation catch is exactly where the emotional layer earns its place. That pause before the polished answer is usually the most honest signal in the whole session. Glad it surfaced something useful for your concept test.

@memetjr2q Thank you for putting it through a real test. That gap between what the transcript says and what the face shows is exactly what Mira is built to catch, most tools only ever see the words.

@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

@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