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Unlock hidden consumer acceptance
Capture first-party consumer sensory signals, model each segment’s sensory identity, and forecast which flavours, textures, and claims will win before you produce. Cut iterations, localize your products with confidence.

Unlock hidden consumer acceptance
Capture first-party consumer sensory signals, model each segment’s sensory identity, and forecast which flavours, textures, and claims will win before you produce. Cut iterations, localize your products with confidence.

Taste preference is like our "Breathing Fingerprint" driven by moods. We digitalised individual's unique sensory identity into regional patterns, so you can predict acceptance, localise products and stay ahead of trends.
Features
Features
Features
Measurable taste, Predictive acceptance, Launch with confidence.
Real-time Sensory Mood Graph Network
Real-time Sensory Mood Graph Network
Real-time Sensory Mood Graph Network
AI-driven Fast, Actionable, Decision-ready Insights
AI-driven Fast, Actionable, Decision-ready Insights
AI-driven Fast, Actionable, Decision-ready Insights
TasteNET™ platform
Capturing first-party dynamic sensory signals and models each segment’s sensory identity. TasteNET™ unifies global sensory data into a normalized, real-time knowledge base and delivers activation ready insights for product, menu, and eCommerce. Privacy-first, non-genetic, and ingredient-agnostic.









Our methdology
Our methdology
Our methdology
Start from sensory identity
Start from sensory identity, not stated intent. We decodes stable taste fingerprints and layers our real-world FlavourMood™ momentum, so your team sees comparable, decision-ready truth across cities, cohorts, and categories. The result is clearer acceptance forecasts and faster, more confident launches.
Sensory perception variance: taste perception differs substantially across individuals by around 27%
Sensory perception variance: taste perception differs substantially across individuals by around 27%
Sensory perception variance: taste perception differs substantially across individuals by around 27%
Internal state influence: mood, satiety, and gut state add measurable variation from time to time (By measured maximum of 15%).
Internal state influence: mood, satiety, and gut state add measurable variation from time to time (By measured maximum of 15%).
Internal state influence: mood, satiety, and gut state add measurable variation from time to time (By measured maximum of 15%).
External context influence: environment, time, and exposure can shift acceptance (By measured maximum of 18%).
External context influence: environment, time, and exposure can shift acceptance (By measured maximum of 18%).
External context influence: environment, time, and exposure can shift acceptance (By measured maximum of 18%).
Modelled regional patterns, one collective sensory identity
Modelled regional patterns, one collective sensory identity
Modelled regional patterns, one collective sensory identity