Language models sound human, but they behave randomly. We give them a character that persists, making human behaviour computable.
Application →
From the same character core come use cases: market research in hours, pricing tests, concept screening and sales training on a synthetic lead. One model, many markets.
Not another chatbot. A reproducible human.
Ask a generic model the same thing a hundred times and you get a hundred different people. We separate decision from language: a mathematical character decides, the language model only phrases. Noise becomes signal, an anecdote becomes a dataset.
Values, risk appetite, price sensitivity and life stage form a stable character core, not a prompt that flips with every answer.
A decision layer fixes the behaviour, a language layer verbalises it. Separated, so nothing gets hallucinated.
Methodology and validation with our scientific advisor Prof. Marko Sarstedt. Models we prove, not claim.
Same question, same character, same answer. Across a thousand twins, real spread emerges instead of randomness.
This is the core: a computable character decides first, then language phrases it. Separated, so nothing gets hallucinated.
Answers scatter randomly. Noise.
Stable segments, real spread. Signal.
Every twin follows the same path: a character is born, decides by inner logic, spreads across a sample, and only then speaks. On the home page we show the flow step by step.
See the genesis of a twin →
