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), select a target market and cohort, and run a simulation against anywhere from 50 AI consumer digital twins for a quick directional check to 1,000 for a full market simulation. Each twin evaluates your product across the 4-stage EPXA 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 and behavioural data collected through our self-reporting foodie community, not through surveys. Our database includes 100,000+ real-world sensory profiles spanning 128 persona types across 118 regions. Each twin carries a measurable taste genotype, flavour preferences, food choice values, and a demographic profile. Unlike generic AI consumer insight platforms that rely on synthetic personas, TasteNET digital twins reflect how people actually perceive and choose food, based on who they are in the 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 tied to the sensory baseline differences.
How does TasteNET help with product reformulation?
First, we diagnose whether it is a formulation problem or a consumer expectation problem. Most of the time the formulation itself is not what went wrong; the context is. When it is about the formulation, TasteNET runs it against each AI consumer's sensory parameters (sweetness and saltiness intensity, texture, and flavour balance) and shows exactly how each change shifts acceptance across segments: which cohorts tolerate a 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. R&D teams use it to pre-screen reformulations before bench trials. GTM teams use it to localise positioning, claims, and channel strategy by market.
Does TasteNET account for real-time market trends and cultural context?
Yes. Every simulation runs against a live World State, 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 processed in a client-isolated environment. TasteNET's AI consumers involve no genetic data: they are derived entirely from our own pipeline and trusted panel partners, with no personal health records, no biometric data, and 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 never stored. For enterprise clients requiring custom data handling, we offer dedicated in-house environments.