Nihilent pitches emotion-sensing AI to sharpen consumer research

Pune-based Nihilent says its nSEPIA platform analyses consumers’ emotional reactions to products, flavours and service experiences, offering marketers an alternative to self-reported survey data. The company says the product has been tested over five years with 30,000 people and is in beta with 2,000 users.

— Source publishedMon, 31 Aug, 2026, 08:45 IST·First seen Mon, 31 Aug, 2026, 09:33 IST·Source ET BrandEquity

What happened

Pune-based Nihilent says its nSEPIA emotion-sensing AI can help marketers measure consumers’ genuine reactions to products, flavours and service experiences,

Key facts

  • 5 years
  • 30,000 people tested
  • 2,000 beta users
  • 85% response rate
  • 4.5 and above accuracy scores

Why this matters

Consumer-insights, CX and martech platforms could view nSEPIA as a potential partnership or tuck-in capability for adding emotion analytics to research and experience-design offerings.

What to watch

  • Named paid pilots or enterprise contracts with major retailers, CPG companies, restaurant groups or research agencies.
  • Evidence that emotion scores predict product trial, conversion, basket size, repeat purchase or campaign lift better than survey-only methods.
  • Disclosure of the input data used by nSEPIA and whether facial, voice, text, video or behavioural signals create biometric-data obligations.
  • Independent validation results, model-bias audits and customer references beyond the reported beta cohort.
  • Regulatory developments on emotion recognition, biometric data, automated profiling and consumer consent in India and target export markets.
  • Expansion from research use into real-time personalization, loyalty segmentation or store-experience monitoring.
  • Run controlled studies comparing nSEPIA outputs with sales lift, repeat purchase, conventional survey scores and human-coded qualitative research.
  • Target CPG category teams, restaurant chains, beauty retailers and quick-service brands where sensory and service reactions materially affect product decisions.
  • Package the platform as a privacy-preserving, opt-in research product with explicit consent workflows, data-minimisation controls and demographic bias audits.
  • Build integrations with research agencies, customer-data platforms and loyalty analytics vendors rather than selling solely as a standalone AI tool.
  • Publish third-party validation of accuracy across languages, regions, age cohorts and consumer contexts to overcome enterprise trust barriers.

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