Electric Twin AI vs Artificial Societies: compared

Artificial Societies models individual reasoning and opinion across connected audiences. Artificial Societies’ January 2026 Survey Evaluation Report records 86% distribution accuracy across 1,000 real-world surveys: 95% of the 91% human self-replication ceiling. Distribution accuracy measures overlap between simulated and human opinion distributions. You can inspect that benchmark before considering how the method fits your audience; it measures survey responses, rather than guaranteeing future behaviour.

We begin with observations of real people. Our method page lists published research, online commentary and reviews, alongside anonymised social data and public records of voting and investment. We reconstruct individual belief systems using psychometric methods, then validate personas against audience benchmarks and prior behaviour. Personas enter networks based on shared characteristics, real relationships and patterns of influence; you can examine results at market, segment or individual level.

A customer-research archive is optional. You can add proprietary data, but diverse observations must exist somewhere to ground the audience. That condition lets us work beyond your customer base while keeping a clear boundary around whom we can model. Where those observations do not exist, there is nothing to ground a persona in.

Electric Twin AI describes an audience built from research you already hold about your customers. Its homepage (opens in a new tab) calls these “Synthetic audiences built from your customer data.” Surveys, focus groups and interviews become a calibrated model that your team can query interactively. For an insight team with a substantial customer archive, that is a specific use for research it has already commissioned.

Electric Twin AI says it evaluates a synthetic audience when onboarding each customer. Its accuracy page (opens in a new tab) describes splitting a dataset, building the audience from one part and withholding the other. It then asks questions the audience has not seen and scores the answers against the real responses. This hold-out procedure gives a buyer a test tied to their own audience, rather than asking them to rely solely on a general benchmark.

The company also states the data requirement directly: “Accuracy depends on the depth of real audience data behind the model.” On that same accuracy page, Electric Twin AI says it may recommend primary research first for very thin or newly formed audiences. The archive’s depth therefore belongs in the buying discussion alongside the questions you want to ask of it.

Artificial Societies and Electric Twin AI describe different starting points for audience construction. We would choose on that basis before comparing interface demonstrations. Our observed-data method suits a decision involving people outside your customer archive. Electric Twin AI’s customer-specific hold-out procedure suits a buyer who wants to establish how a twin reproduces existing customer research.

We recommend Electric Twin AI for repeated research on a deep customer archive. The table compares that task with our method’s starting point: observed people and their relationships.

Sources: our method and evaluation, including the January 2026 Survey Evaluation Report; Electric Twin AI’s homepage (opens in a new tab) and accuracy page (opens in a new tab).

For a stakeholder decision, look at whom the study must represent. Artificial Societies’ earnings-call case study built four societies around the company’s stock: sell-side analysts, buy-side investors, the corporate ecosystem and financial media. For that work, we would organise a study around the interests and relationships represented by those societies.

Electric Twin AI’s published percentages and our distribution-overlap result cannot establish an accuracy ranking. Electric Twin AI’s accuracy page (opens in a new tab) reports up to 96% on its average-answer measure, labelled 1-MAE, and up to 92% on NDAM, its measure of the whole response distribution. It says the latter retains its meaning as the number of answer options changes. Its stated human benchmark is around 94% agreement when people answer the same question again.

Electric Twin AI explains both measures and publishes the lower result. Its accuracy page reports more than 50,000 evaluations across 155 countries, while its 11 February 2026 accuracy post (opens in a new tab) describes an evaluation set of 11,000 unique personas spanning UK and US populations. Those are different descriptions of evaluation scale. A buyer should request the test relevant to their own audience, rather than turn either total into a proposed study size.

Our January 2026 Survey Evaluation Report also examines the answers beneath an aggregate distribution. Artificial Societies’ self-contradiction rate is under 2% in that report; its evaluation page puts human panels at about 9%. That measure counts personas contradicting themselves; it does not measure factual errors about the outside world. Artificial Societies’ internal coherence is 89% in the same report; its evaluation page sets that against a human-panel band of 60–95%. This tests whether answers to questions about the same underlying attitude agree with one another.

The report’s 93% open-response quality measures distinct-word richness against 120,000 human social-media posts, with human social media at 91%. Language variety matters when you read an explanation behind an answer, but it does not establish that the explanation is correct or predictive. Our September 2026 validity framework explains why a matching answer distribution also needs tests of relationships between beliefs.

Peer review has a separate scope. The British Journal of Psychology paper (opens in a new tab), published online in December 2024, found communities forming around shared language and similar content in simulated chatbot societies. It supports modelling homophily, the tendency to form communities with similar others. Artificial Societies’ survey figures come from the January 2026 evaluation report, not from that paper.

Electric Twin AI is our recommendation when your team has a deep survey archive and the proposed work concerns that customer base. Its published onboarding method (opens in a new tab) describes both how the audience is built and how its responses are checked against hidden customer data. Ask to see that evaluation for the audience you intend to use. A result tied to your own research is a useful basis for deciding whether to proceed.

Query volume is another reason to consider it. Electric Twin AI’s homepage (opens in a new tab) describes a licensing fee for unlimited queries, priced by audience size and team seats. If your team’s requirement is repeated questioning of the same customer audience, that stated commercial model belongs in the proposal discussion. We would ask for the audience and seat scope in writing before assessing its value.

Electric Twin AI also gives buyers a public account of how to interpret its measurement: metric definitions, a human benchmark and reasons for reporting the lower score. We regard that as useful disclosure. Its accuracy page (opens in a new tab) says, “There are questions we will tell you to take to human respondents instead.” That is a sensible boundary to discuss with a vendor before relying on its answers.

Artificial Societies fits a confidential strategy decision that depends on audiences you cannot put the draft in front of. In the Teneo engagement, in late 2025, Artificial Societies tested six technology narratives across three societies representing consumers, industry peers and policymakers. The study produced 189,756 responses and delivered a written report with an interactive platform for examining approval scores and verbatim reactions. Those deliverables let the team assess the proposed narrative by audience.

Teneo’s own account of the engagement is in our Teneo case study; it describes that engagement rather than a claim that simulation should replace human research generally.

Our hyperscaler earnings-call study gives a different reason to choose us: testing a team’s assumptions before the event. The company expected a margin reset to dominate its call. Simulated analysts instead pressed on demand durability and financing commitments, and the study reports that the live questions followed that pattern. The client received a pre-call briefing and access to rerun scenarios as the numbers changed.

The earnings-call engagement delivered results in 48 hours, according to the case study, with drafts and numbers kept inside Artificial Societies’ private environment. The duration belongs to that engagement; the comparison with the live discussion gives an investor-relations buyer a separate piece of evidence to examine.

Artificial Societies needs diverse observations of the audience it simulates, whether public or client-provided. Our method draws on behaviour and commentary as well as published research. You can therefore discuss a policy audience or an investor audience without first holding a survey archive of those people. The requirement is evidence about the people whose opinions matter to the decision.

Artificial Societies’ transport-policy study describes a society of 1,500 Washington D.C. opinion leaders, matched to real individuals using public data and social activity. The study covered more than 360 organisations and 800 distinct job titles. We tested competing narratives and proof points, then exposed the simulated audience to damaging coverage and hypothetical scenarios. The client could inspect reactions by segment and individual persona.

The point of that design was to test a narrative before exposing it to the people who could change the debate. The transport-policy case study records that showing draft messages to the intended audience would itself have changed the situation under study. It is evidence for considering simulation when live exposure carries consequences, provided the audience has enough observed data to ground it.

Ask Electric Twin AI to show the hidden-data comparison described on its accuracy page (opens in a new tab), using the customer audience your proposed work concerns. Ask us to show how a society’s evidence and belief systems relate to your intended audience, and how results change when you alter a message. Our September 2026 validity framework sets out tests involving question order, rephrasing and changes to supporting evidence; how to evaluate a synthetic research vendor covers all eight of its tests.

For Artificial Societies, we would make the relationships between answers part of the demonstration. Matching the number of people who select each answer does not establish who holds which combination of beliefs. The framework therefore examines whether related questions capture the intended attitude and whether findings hold for real populations. That matters if you plan to use segment differences to decide what to say.

Keep the decision’s evidence requirement in view. Electric Twin AI states (opens in a new tab) that regulated claims and legally mandated consumer testing need real people on record. Our validity framework describes the gap between stated intentions and actual behaviour, which survey-based simulation inherits. A forecast of how people answer a message should inform the decision within that scope; it should not be treated as an observation of what they subsequently did.

We publish this comparison using the vendor pages, evaluation report, research papers and case studies listed below. The materials were checked on 17 September 2026. We compare audience inputs, validation and task fit; this article reports published evidence rather than results from a product trial. Electric Twin AI’s claims retain attribution to the company, and our recommendations express our judgement about the work each published method suits.

Information is believed accurate at the source dates. Trade marks and company names identify the subjects of comparison; their use implies no affiliation or endorsement. Send corrections to support@societies.ai. Commercial quotations and delivery commitments sit outside this comparison and should be agreed for the proposed engagement.