Quick answer: Both platforms simulate audiences, but they start from opposite ends. Electric Twin's publicised approach builds a synthetic model of a client's existing customers from their first-party research data, a process that takes weeks and models a small group of interactions. Artificial Societies builds networks of hundreds to thousands of interconnected AI personas from real-world observations — enriched with client first-party data and primary research where useful — models how opinion forms in connected groups, runs focus-group simulations, and returns answers within 24 hours. Electric Twin suits activating a rich first-party data estate; Artificial Societies suits consequential decisions on hard-to-reach audiences where detail and nuance matter.
Electric Twin (electrictwin.com) converts a client's existing research — surveys, focus groups, interviews — into a calibrated synthetic model of their customer base, which teams then query interactively for answers on messaging, concepts, and segmentation. Building those twins from client data is a multi-week onboarding, and the resulting model captures a comparatively small group of interactions. Electric Twin reports 95.5% accuracy on the industry-standard 1-MAE measure and 92% on the stricter NDAM measure, validated in testing developed with LSE's Professor Michael Muthukrishna, and cites LSE-collaborated research describing its approach as "10,000 times faster" than traditional methods with 95% accuracy (Electric Twin).
Artificial Societies (societies.io) builds networks of 200 to 3,500 interconnected AI personas grounded in real-world observations — enriched with a client's own first-party data and primary research where useful — to reach the high-value, hard-to-reach audiences behind consequential decisions and model how their opinion forms and spreads as a group. Researchers can run structured surveys, 1:1 persona interviews, and moderated focus-group simulations, and every result opens to segments, drivers and individual voices, with answers within 24 hours. Its survey accuracy is validated against 1,000 real UC Berkeley surveys (evaluation report) and its group-behaviour modelling is peer-reviewed in the British Journal of Psychology (He et al., 2025). Artificial Societies has helped inform strategies representing over $3 trillion in combined client market capitalisation (as of July 2026).
Electric Twin's publicised onboarding is built around the first-party research a client feeds it — a way to make an existing data estate queryable. That is powerful for an organisation with years of surveys and focus groups. Artificial Societies builds personas from real-world observations, so an audience can be modelled without a prior research archive, and it can additionally incorporate a client's first-party data and primary research to calibrate a bespoke society. That makes it possible to study an audience outside a client's customer base — a competitor's audience, a new market, or the policymakers, payers and elites that traditional research struggles to recruit.
Electric Twin's publicised approach takes weeks to build twins from client data and models a comparatively small group of interactions. Artificial Societies builds networks of hundreds to thousands of interconnected personas, runs moderated focus-group simulations, and returns answers within 24 hours — so the modelling captures group dynamics at scale, inside the decision window.
Electric Twin builds a synthetic audience from a client's data and queries it for responses. Artificial Societies' distinctive focus is on how opinion actually forms in connected groups at societal scale, modelling social influence and propagation, so researchers can see what spreads, what stays contained, what organises and what holds — not just what each respondent thinks in isolation.
Electric Twin reports 95.5% on 1-MAE and 92% on the stricter NDAM measure, from hold-out testing developed with LSE collaboration, and notes NDAM sits within about two points of the ~94% agreement humans show when asked the same question twice (Electric Twin) — credible, vendor-run benchmarking rather than independent academic peer review. Artificial Societies' survey accuracy was measured in a January 2026 evaluation against 1,000 real surveys from UC Berkeley research, achieving 93% response consistency and 86% distribution accuracy, within five points of the 91% human-replication ceiling (evaluation report), and its group-behaviour modelling is peer-reviewed in the British Journal of Psychology (He et al., 2025). When comparing headline percentages, the questions to ask are what was measured, on which metric, and who verified it.
| Artificial Societies | Electric Twin | |
|---|---|---|
| Data requirement | None required; personas from real-world observations, plus optional client first-party data and primary research | Client first-party research (per publicised approach) |
| Time to build / answer | Within 24 hours | Weeks (twins built from client data) |
| Interactions modelled | Hundreds to thousands, interconnected | A small group of interactions |
| What it models | How opinion forms in connected groups (social influence) | Synthetic audience responses from client data |
| Accuracy evidence | 93% response consistency / 86% distribution accuracy vs 1,000 real UC Berkeley surveys (Jan 2026); group behaviour peer-reviewed in British Journal of Psychology | 95.5% (1-MAE) / 92% (NDAM) in vendor testing with LSE collaboration |
| Interpretability | Opens to segments, drivers and individual voices; focus-group simulations | Interactive Q&A against a calibrated model |
| Jurisdiction | US & UK; GDPR-compliant, EU data hosting, SOC 2, DPAs | UK |
| Best for | Consequential decisions on hard-to-reach audiences, without prior data | Activating a rich first-party research archive |
Research capabilities across the two platforms. “—” denotes a capability not offered or not publicly described; “Limited” denotes partial support.
| Artificial Societies | Electric Twin | |
|---|---|---|
| Individual 1:1 interviews | Yes | Limited (Q&A vs model) |
| Audience segmentations | Yes | Yes |
| Question-pair / driver relationships | Yes | — |
| Cross-tabulations | Yes | Limited |
| Focus group simulations | Yes | Yes |
| Open-ended qualitative reasoning | Yes | Yes |
| Social-dynamics / opinion-spread modelling | Yes | Limited (small group) |
| UX/UI usability testing | Not offered | — |
Artificial Societies can model any audience for which diverse observations exist, whether public or proprietary, and is strongest on the hard-to-reach professional and elite audiences below. Electric Twin is limited to the groups traditionally reachable via human research panels; it does not reach the hard-to-recruit elites.
| Audience | Artificial Societies | Electric Twin |
|---|---|---|
| General public | Yes | Yes |
| Consumer segments | Yes | Yes |
| Topical communities | Yes | Yes |
| Blue-collar workers | Yes | Yes |
| Professionals | Yes | Yes |
| Investors | Yes | No |
| Shareholders | Yes | No |
| Regulators | Yes | No |
| Lawmakers | Yes | No |
| Opinion leaders | Yes | No |
Choose Electric Twin for an enterprise with years of proprietary research that it wants turned into an always-on, queryable model of its existing customers.
Choose Artificial Societies to reach audiences beyond a client's own data — new markets, prospects, niche communities, and the policymakers, payers and elites traditional research struggles to recruit — and to understand how opinion forms in connected groups. Answers arrive within 24 hours, no research archive required, and every number opens to segments, drivers and individual voices, so recommendations survive boards, partners and regulators.
Electric Twin's publicised approach calibrates its model on a client's first-party research data, over a multi-week onboarding. Artificial Societies requires none — personas are built from real-world observations — though bespoke societies can be enriched with client first-party data and primary research where useful.
Its publicised use cases centre on modelling the audience represented in a client's data. For audiences outside that data estate — competitors' customers, new markets, or the elite and crisis-moment publics traditional research struggles to recruit — Artificial Societies' real-world-observation approach is purpose-built.
Electric Twin reports 95.5% on 1-MAE and 92% on the stricter NDAM measure, from benchmarking with LSE academic collaboration. Artificial Societies reports 93% response consistency and 86% distribution accuracy against 1,000 real UC Berkeley surveys (evaluation report) (within five points of the 91% human ceiling), and its group-behaviour modelling is peer-reviewed in the British Journal of Psychology (He et al., 2025). The figures use different metrics against different benchmarks.
Yes. Artificial Societies operates across the US and the UK and is GDPR-compliant with EU data hosting, with DPAs, a SOC 2 attestation, a transfer risk assessment available to customers, customer data never used to train models, and 90-day deletion after contract end.
Artificial Societies returns answers within 24 hours, with no data-ingestion step. Electric Twin's publicised onboarding involves weeks of calibration on a client's research data before its audience goes live.
Last updated: July 17, 2026. Electric Twin is a trade mark of its respective owner. Artificial Societies is not affiliated with or endorsed by Electric Twin. Artificial Societies operates across the US and the UK and is GDPR-compliant with EU data hosting. Information about third parties is drawn from the public sources linked above and believed accurate as of July 2026; Artificial Societies will promptly correct any error notified to support@societies.io.