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Frequently Asked Questions
What is TasteNET Simulator and how does it work?
TasteNET is an AI consumer simulation platform built specifically for the food and beverage industry using digital twin technology. You upload your product details: ingredients, flavour profile, positioning, and GTM information etc., select a target market and cohort, and run a simulation against 50 to 1,000 AI consumers . Each one evaluates your product across a 4-stage acceptance funnel: Exposure, Purchase, Experience, and Advocacy. You receive a full acceptance report with segment-level diagnostics in under 30 minutes.
What data are the digital twins built on?
Every digital twin is grounded in first-party sensory & behavioural data collected through self-reporting foodie community, not surveys. Our database includes 90,000+ real-world sensory profiles spanning 128 persona types across 118 regions. Each twin carries a measurable taste genotype, flavour preference, food choice values, and demographic profile. Unlike generic AI consumer insight platforms that rely on synthetic data , TasteNET digital twins reflect how people actually perceive and choose food, based on who they are in real world.
Can TasteNET predict product acceptance in different countries and cities?
Yes. TasteNET maintains a Sensory Map with region-specific flavour rules, intensity thresholds, and taste baselines for 118 regions across 38 countries. Each region's profile includes primary flavour rules (e.g. "Plateau Umami Cumin Heat" for Qinghai, China), flavour keywords, and dynamic sensory trends updated from multi-source market signals. When you run a simulation, twins respond within the sensory and cultural context of their region. So the same product may score 85% acceptance in Singapore and 62% in Mexico City, with clear reasons why due to the sensory baseline difference.
How does TasteNET help with product reformulation?
First, we need to see if its the formation problem or its the consumer expectation problem. Most of the time its not the formulation itself that went wrong, instead its the contextual part. If its really about the formulation, TasteNET run your formulation against AI consumer's sensory parameters (e.g. sweetness, saltiness intensity, texture, and flavour balance), shows you exactly how each change shifts acceptance across segments: which cohorts tolerate 20% sugar reduction without noticing, and which perceive it as a downgrade.
Who uses TasteNET, and how does it fit into existing product development workflows?
TasteNET is used by Consumer Insights, R&D, and Go-to-Market teams at CPG and F&B companies. Insights teams use it for pre-launch validation, concept screening, and iteration.
Does TasteNET account for real-time market trends and cultural context?
Yes. Every simulation runs against a live "World State", which is a continuously updated layer of cultural signals, trending flavours, health narratives, and social dynamics specific to your target market. For example, if "Heritage Detox Brew" is trending in China in February 2026, digital twins in that region will factor this cultural momentum into their purchase and advocacy decisions, so simulations reflect the market as it is today.
How does TasteNET handle data privacy and security?
All product data uploaded for simulation is client-isolated environment. TasteNET's AI consumers are non-genetic and derived entirely from our pipeline and trusted panel partners, no personal health records, no biometric data, no DNA. The platform is privacy-first by design: sensory profiles are anonymised and aggregated at cohort level, and ingredient-agnostic, meaning proprietary formulations are processed but not stored. For enterprise clients requiring custom data handling, we offer dedicated in house environments.