Artificial Societies

About Artificial Societies

Artificial Societies simulates how a specific audience will react to something before you put it in front of real people. Rather than surveying a panel, it builds networks of AI personas modeled on what real people say and do, then runs the message, product concept, or policy position through that network and reports how opinion forms and spreads. The network modelling is the distinguishing claim — most synthetic-respondent tools poll independent personas in isolation, which misses the fact that opinions in a real audience are shaped by who else in the group has already reacted. The company was founded by Oxbridge social and data scientists who authored the first large-scale AI society paper, and it publishes its numbers: 2.5 million-plus personas, 95% accuracy against human self-replication with a linked evaluation, and 24-hour turnaround on insights backed by millions of simulated responses. The flagship use cases are all high-consequence decisions where testing on real humans is slow, expensive, or risky: strategic communications, crisis and reputation modelling across media, regulator, and stakeholder reactions, government affairs positioning with policymakers and regulators, innovation testing with hard-to-reach buyers and early adopters, and investor relations. Zero exposure is a stated feature — pre-release and sensitive strategies can be tested without anyone outside seeing them. A published case study covers Teneo simulating 180,000+ human perspectives.

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