Lavakumar E

Lavakumar E

Decode by EntropikDecode by Entropik
Founder at Entropik | Building Decode

About

I started Entropik because brands kept making big decisions based on what consumers said, not what they actually felt. We built Decode to close that gap. Our latest product is Mira, an AI moderator that runs qualitative interviews at scale and reads emotional and behavioral signals in real time. Facial expressions, voice tone, behavioral cues, the signals that traditional research systematically misses. 5x faster than traditional research. 70+ languages. Trusted by 150+ global brands including Unilever, Nestlé, and PepsiCo. Backed by Bharat Innovation Fund, Bessemer Venture Partners, and SIG Venture Capital.

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Maker History

  • Decode by Entropik
    Decode by EntropikKnow what they feel. Decide what to build, ship, or sell.
    Sep 2026
  • Mira by Decode
    Mira by DecodeAI moderated interviews that read how people feel
    Jul 2026
  • AffectLab
    AffectLabMeasure emotions to improve marketing ROI
    Nov 2018
  • 🎉
    Joined Product HuntJanuary 29th, 2018

Forums

8h ago

AI Creative Insights by Decode - Predict which ad wins before you spend on media

Upload any ad, banner, OOH, or video creative and AI Creative Insights predicts how people will respond before your media budget goes live. Get attention heatmaps showing where eyes land first, second-by-second emotion curves, and prescriptive suggestions on what to change and why. Benchmark against category norms, compare variants side by side, and use Synthetic Audience to see predictions persona by persona. Built by Entropik, the team behind Decode.

2mo ago

Which newsletters are worth following in the business and marketing space?

I know each of you probably has a rich list of online creators and publications that provide you with interesting ideas and useful tips. So I thought it would be a good opportunity to exchange recommendations.

For me, these are:

  • Ad Professor

  • Tom s Marketing Ideas

  • PH Newsletter

Feel free to share your own list as well.

What do transcripts miss that sends you back to the recording?

Nine years of working closely with insights teams across CPG, BFSI, and tech has given us a consistent observation: the transcript is where researchers start, but the recording is where they go when the transcript isn't enough.
They go back to hear how someone said something. To check whether the pause before an answer was genuine uncertainty or just thinking time. To see whether the person's expression changed when they described a feature they claimed to like. To catch the moment when engagement visibly dropped when they stopped leaning forward, when their eyes moved away from the screen even while their words stayed polite and positive.
That gap between the transcript and the recording is where a significant amount of research insight lives. And it's also where the most time gets consumed in manual analysis. Researchers who run 20 or 30 interviews know what it's like to sit through hours of footage looking for the three or four moments that actually matter.
This observation that the transcript captures what was said but not how or with what underlying feeling is fundamentally what drove us to build the emotion and behavioural layer into Mira. Not to replace researcher interpretation, but to give researchers a way to find the right moments faster.
I'm genuinely curious: what's the signal you find yourself wishing you had after you've finished going through a set of interviews? What consistently drives you back to the recording that the transcript alone doesn't give you? And have you found any workarounds annotation practices, tagging systems, collaborative review setups that help you capture more of what the transcript loses?

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