Understanding consumers should NOT be a set of averaged, static metrics.
It should be a reconstruction: one that preserves each consumer's full-dimensional taste preferences, behavioural patterns, and state-dependent responses, available to query at any time. That is what TasteNET is built to do.
Trusted by global F&B industry leaders
TasteNET gives F&B innovation teams high-fidelity AI consumer digital twins, grounded in real-time, first-party consumer data.
Key features
The sensory intelligence engine behind every digital twin
Built on real-world consumer data, not synthetic personas
TasteNET Simulator is a market behaviour simulator built on real-world taste preferences and psychological decision models. Every digital twin is grounded in first-party sensory profiles, never synthetic data. The twin population spans 128 persona types across 118 regions in 38 countries. The platform is privacy-first and ingredient-agnostic: proprietary formulations are processed but never stored.
Our methodology
Start from sensory identity
Traditional consumer research asks what people think. TasteNET measures the drivers behind the answers. Psychophysics captures each person's stable sensory fingerprint: detection thresholds, JAR windows, and preference patterns. Thermodynamics explains why those preferences hold: every food decision seeks a lower-energy equilibrium, where sensory reward outweighs the cost of processing.
Sensory perception variance: individuals differ by up to 27%, and each occupies a different point on the perception landscape.
Internal state: mood and body state shift the equilibrium by up to 15%, a range surveys cannot capture.
External context: environment and cultural exposure displace thresholds by up to 18%, so static reports chase a moving target.
