COMPANY · OUR APPROACH
Making dialogue possible between decision-makers and the people their decisions affect.
Imagine All The People was born from a belief: every decision that affects people should be preceded by a real dialogue with those people. Since that dialogue is impossible at the scale of the major decisions we help prepare, we make it possible at synthetic scale.
THE FOUNDING BELIEF
Create synthetic people to make dialogue possible at scale.
The organizations we work with make decisions every day for real populations (consumers, citizens, patients, employees, voters) without always having truly listened to them: it is too expensive, too slow, or conventional samples lack the necessary granularity.
This absence of dialogue has a measurable cost: public policies that fail on friction points no study had detected, campaigns that trigger rejection that could have been anticipated, reforms that set off crises public debate had not foreseen.
Imagine All The People was created to address this absence of dialogue. Since human dialogue at this scale is impossible in real life, we make it possible at synthetic scale. Our synthetic people are neither real people nor statistical averages: they are individuals modeled with enough granularity to make genuine dialogue possible, with questions asked, answers returned and contextual follow-ups.
This ambition is not only methodological. It is fundamentally ethical and political. It rests on the belief that decision-makers who affect populations should be able to speak with those populations before deciding, rather than suffer the consequences of not having listened to them. It rests on the belief that the best decisions are those that account for people in all their complexity instead of reducing them to averages and extrapolations. It rests on the belief that artificial intelligence, used properly, can reconnect decision-making with people rather than push them further apart.
THE DECISION PATH
From decision to trade-off, in five steps.
01
Decision
The structuring decision to prepare.
02
Population
Synthetic humans generated under constraints.
03
Simulation
Interviews conducted individual by individual.
04
Reactions
Motivations restituted in their own words.
05
Trade-off
Material traceable back to its origin.
FROM BELIEF TO PRACTICAL VALUE
What synthetic dialogue makes possible for decision-makers in practice.
Our founding belief translates into concrete uses that transform how major decisions are prepared, weighed and implemented. Five uses illustrate this shift particularly well.
Anticipate rather than react
A decision prepared in dialogue with the people it will affect can anticipate rejection, resistance and misunderstanding. An industrial transformation plan tested in advance with the populations concerned identifies friction points before they become crises. A public policy informed by prior synthetic dialogue avoids failures that human dialogue could have revealed, had such dialogue been possible.
Listen to diversity rather than averages
Synthetic people are not reducible to average typologies. They have their own motivations, internal contradictions, personal trajectories and ambivalences that escape conventional statistical categories. This granularity changes the nature of the evidence available for decisions: decision-makers gain access to the real diversity of the populations concerned, not a simplified aggregation of them.
Decide with the rigor of sociology and the sophistication of AI
Our synthetic populations combine the methodological rigor of quantitative sociology (calibration on public data, variance measurement on benchmarks, challengeability of results) with the technical sophistication of language models (dynamic interviews, rendering motivations in respondents’ own language, contextual follow-ups). This combination establishes a new methodological category, synthetic dialogue, which does not exist in any prior tradition taken on its own.
Trace every conclusion back to its source
Every insight we deliver can be traced back to the individual interviews that produced it. Every projection is supported by the reasoning paths that built it. Every dominant scenario can be explained in the actual terms of the synthetic people concerned. This end-to-end traceability turns results into material that independent third parties can challenge, which is structurally reassuring for legal departments, ethics committees and sector regulators.
Reconnect decision-making and people
Beyond tactical uses, our system restores a relationship between decision-makers and the populations they affect. This restoration is not cosmetic: it is structurally democratic. It makes possible what the acceleration of issues, the growing complexity of societies and the fragmentation of populations had gradually made impossible: genuine dialogue between those who decide and those who bear or benefit from the consequences.
THE THREE PILLARS OF OUR INSTITUTIONAL POSITIONING
Ethical responsibility, scientific rigor, European roots.
Our founding belief extends into three institutional pillars that shape our position in the applied AI ecosystem. Their order is deliberate: people first, rigor second, territory third.
Ethical responsibility by design
Because we create synthetic people that inform consequential decisions, our ethical responsibility is not decorative: it is part of the discipline itself. We reject discriminatory uses that could cause avoidable social harm. We reject algorithmic opacity that would prevent democratic challenge. We reject systems that would replace human judgment rather than inform it. Our ethical governance is documented publicly in the Trust section and takes concrete form through end-to-end traceability, challengeability by independent third parties, our position regarding the European AI Act, and ongoing dialogue with the bodies shaping AI accountability in Europe.
An assertive commitment to scientific rigor
We reject the posture of actors that build credibility on methodological opacity. Our discipline is documented in open scientific archives (ArXiv), in leading journals in the field (Frontiers in Artificial Intelligence), and in dialogue with leading academic teams (EPFL, Sorbonne Université, Université de Bologne, Université Côte d'Azur). Our benchmarks are quantified, our calibration protocols are documented, and our limitations are stated publicly. This scientific commitment is not only methodological: it is fundamentally political. It establishes a radically different posture from platforms that refuse to document their methods and claim that opacity is protective.
A structural European grounding
Imagine All The People is a French and European company: French capital and governance, infrastructure hosted in France, no dependence on non-European actors for critical processing, and legal sovereignty in the face of US and Chinese extraterritorial regimes. This grounding is not a marketing claim: it is structural. It places Imagine All The People within Europe’s effort to reclaim digital sovereignty from the US and Asian SaaS platforms that currently dominate applied AI. It makes our company an instrument of European strategic autonomy in a field Europe cannot afford to abandon.
DEFINING OURSELVES BY WHAT WE ARE NOT
Four dominant categories we deliberately distinguish ourselves from.
Our institutional positioning can also be understood through what we refuse to become. Four dominant categories of applied AI illustrate these deliberate distinctions.
WHAT WE ARE NOT
- A SaaS social-listening platform
- A traditional research agency
- A general-purpose language model
- A manipulation or disinformation tool
WHAT WE BUILD
- Calibrated synthetic populations, sovereign French infrastructure, published methodology
- A methodology that complements rather than replaces traditional research
- Language models combined with a mathematical constrained-generation core
- A listening instrument for decision-makers, not an influence instrument on real populations
We are not a social-listening SaaS platform
US social-listening SaaS platforms use the digital traces people leave on social networks to produce consumer insights. This creates three structural problems: representativeness bias (only some segments express themselves on social networks), legal extraterritoriality (US hosting, strategic data potentially accessible under the Cloud Act), and methodological opacity (unpublished proprietary algorithms). We are building a structurally different alternative: synthetic populations calibrated on public and proprietary data, sovereign French infrastructure, and methodology published in open scientific archives.
We are not a traditional research firm
Traditional research firms use external panels, standardized questionnaires and synthesized findings. This methodology has demonstrated its value for decades, but it cannot match the pace of today’s major decisions or the granularity of the populations affected by them. We complement rather than replace this methodology: conventional research firms remain essential for major strategic studies, while our system addresses cases where their methodology reaches its limits (short timelines, fine granularity, continuous comparisons, rapid iteration).
We are not a general-purpose language model
General-purpose language models (GPT, Claude, Gemini and equivalents) can produce plausible outputs on almost any topic, but without calibration on real populations, traceable reasoning, or variance measurement against verifiable benchmarks. These models remain valuable tools in our technical stack, and we use them to conduct qualitative interviews, but they do not constitute a rigorous decision methodology on their own. Our differentiation lies precisely in combining these models with a mathematical foundation for constrained generation that guarantees the representativeness of our synthetic populations.
We are not a manipulation or disinformation tool
Our system is not designed to produce content intended to influence real populations without their knowledge: deceptive communications, disinformation campaigns or behavioral manipulation. Our synthetic populations are a listening instrument for decision-makers, not an influence instrument aimed at real populations. This distinction is structurally important and documented in our ethical governance protocols: uses identified as manipulative are contractually refused, without exception.
WHERE WE WANT TO TAKE IMAGINE ALL THE PEOPLE
Our institutional ambition for the years ahead.
Our institutional ambition goes beyond building a commercially successful company. We pursue a broader goal: establish populations of synthetic people as a legitimate methodological category in the European decision-making ecosystem, in dialogue with the actors shaping accountability in artificial intelligence.
Help structure an emerging scientific discipline
Synthetic populations are an emerging scientific discipline still taking shape. Our co-editing of the Research Topic Synthetic Populations in Frontiers in Artificial Intelligence, our ArXiv publications, and our academic collaborations with EPFL, Sorbonne Université, the Université de Bologne and Université Côte d'Azur contribute to that development. Our ambition is to establish this discipline as a recognized scientific field over the coming decade, with its own conferences, journals, doctoral programs and shared methodological standards.
Contribute to European digital sovereignty
Artificial intelligence has become a major issue of strategic sovereignty for Europe. Europe cannot afford to leave this critical technology to the US and Asian SaaS platforms that currently dominate it. Our ambition is to contribute to a sovereign European AI ecosystem, in collaboration with other European actors in the field and within structuring initiatives such as Gaia-X, SecNumCloud, Horizon Europe projects and France 2030 initiatives. Our French and European commitment is structural, not opportunistic.
Deepen our ethical responsibility in a sensitive field
The ethical stakes of our discipline are substantial: democratic representation of synthetic populations, challengeability of consequential decisions they help inform, prevention of discriminatory or manipulative uses, and dialogue with regulators. Our ambition is to deepen our ethical responsibility over the coming years through formalization of our ethics committee, engagement with European AI regulatory bodies, publication of our governance protocols, and ongoing dialogue with sector regulators.
Make accessible what should be structurally accessible
Our commercial ambition must remain consistent with our founding belief. We want to make our populations of synthetic people accessible not only to large listed companies and major public administrations that can structurally afford them, but also to smaller actors with a structural need for dialogue with the populations they affect: local authorities, innovative SMEs, associations, independent researchers and investigative journalists. Expanding access progressively is a core ambition for the coming years: we do not want to build a tool reserved for the most powerful actors.
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Our approach sets out Imagine All The People's founding belief. Our story recounts the stages of the company's construction. The team introduces the people who carry our work forward. Commitments documents the structuring commitments we make publicly. For any institutional, press or partnership enquiry, our team is available.
