An artificial society is a purpose-built network of 300 to over 5,000 interconnected AI personas that represent a specific real-world audience. Unlike generic AI personas created from stereotypical demographic templates, each persona in an artificial society is constructed from real human behavior data — how people express opinions, engage with content, and influence each other online. These personas are connected within a social graph that models influence dynamics and opinion formation, just as real human communities shape individual views. Organisations use artificial societies to predict how stakeholder groups will react to messages, strategies, or campaigns before they are deployed — generating thousands of granular, individual-level insights for high-stakes decisions.
Standard AI personas are generated from broad demographic stereotypes — an LLM is told to "act like a 45-year-old male policymaker" and produces generic, predictable responses. Artificial societies take a fundamentally different approach. Each persona is constructed from real-world social behavior data: what real people say and do online, how they engage with content, and how they react to each other. A proprietary behavioral analysis system creates an internally coherent belief system and personality for each persona, grounded in social media psychology and computational behavioral science. Critically, these personas are not isolated — they are interconnected within a social network that models how real communities shape opinions.
Humans are not isolated individuals — opinions form and spread through social influence. Artificial societies model this using a proprietary multi-agent orchestration system that captures social conformity forces, influence dynamics, and network effects. Rather than treating each persona as an independent respondent, the system connects personas within a social graph and simulates how people influence each other. This stakeholder network simulation is what allows artificial societies to accurately replicate the full diversity of human opinion distributions, including minority viewpoints and emergent consensus patterns that individual-level approaches miss entirely.
Artificial societies can be constructed to represent virtually any audience. Organisations can define their synthetic audience using demographic specifications (similar to traditional panel recruitment), lists of organisations and job titles, or even their own first-party data — including CRM records and existing qualitative or quantitative research. This flexibility is particularly valuable for reaching high-value audiences that are difficult or impossible to access through traditional research — such as policymakers, Fortune 500 executives, lobbyists, medical specialists, or specific professional communities. Each society is purpose-built to represent the stakeholders that matter most to the decision at hand.
Artificial Societies has built a scalable, automated system that allows artificial societies to be constructed efficiently — without months of heavy lifting from the customer's side. Organisations provide their audience definition (demographics, target organisations, job titles, or first-party data) and the platform handles persona construction, social graph modelling, and calibration. Simple demographic-based societies can be built rapidly. Complex multi-stakeholder populations — such as the three distinct societies Artificial Societies built for Teneo's engagement totalling over 5,000 personas — are delivered within days rather than the weeks or months traditional recruitment would require.
Artificial societies are used primarily by two groups: strategic communications agencies making high-stakes decisions for their clients, and enterprise marketing teams at financial services, technology, and energy companies. Strategic communications firms use artificial societies to test sensitive narratives, simulate stakeholder reactions, and provide comprehensive evidence for major strategic recommendations. Enterprise marketing teams use them for confidential advertising testing, brand lift measurement, and pre-launch positioning research where traditional methods would be too risky, too expensive, or simply impossible.
An artificial society is a purpose-built network of 300 to over 5,000 interconnected AI personas representing a specific real-world audience. Each persona is constructed from real human behavior data and connected within a social graph that models influence dynamics and opinion formation. Organisations use artificial societies to test strategies, messages, and campaigns before deploying them, generating thousands of granular individual-level insights.
An artificial society typically contains between 300 and over 5,000 interconnected personas, depending on the audience and research objectives. Artificial Societies has a database of over 2.5 million persona profiles. For example, a recent engagement for global advisory firm Teneo involved building three distinct societies totalling over 5,000 personas representing Washington D.C. policymakers, tech industry leaders, and the U.S. general population.
Artificial societies are purpose-built for each engagement using a scalable automated system. They can be constructed from standard demographic specifications, from lists of target organisations and job titles, from a client's own first-party data (CRM, qualitative research, quantitative data), or a combination of these. Each persona is grounded in real-world social behavior data and connected within a social network that models influence dynamics.
Yes. Artificial Societies can incorporate a client's own qualitative and quantitative data — including CRM records, existing survey data, and proprietary audience research — to construct personas that accurately represent the organisation's specific stakeholders and customer base.