Synthetic-People calibrates digital twins that simulate how real people make decisions.

They are not scripted archetypes. They are built on real consumer actions, neuroscience signals, and thousands of deep, observed contexts.

Traditional research captures what people say after they rationalise a choice. Synthetic-People model the decision while it is still forming — before it is cleaned into a coherent story.

Because real decisions are not neat. People hesitate. Contradict themselves. Justify in hindsight. We preserve that messiness so insight reflects how decisions are actually made, not how they are explained.

Synthetic-People uses its proprietary Grounded Intelligence framework, integrating multiple calibrated layers:

— Real-world consumer action signals (purchases, digital activity, intent patterns) — Emotional and physiological response inputs (neuroscience-aligned signals) — Historical research calibration (5,000+ validated studies and observed decision contexts) — Behaviour-focused language models trained on real decision patterns

Actions anchor reality. Emotions capture hidden drivers. Historical research grounds context. Behavioural models simulate decision logic.

Together, they produce responses that are context-aware, psychologically consistent, and traceable — grounded in aggregated, ethically sourced data, never personal data.

We don't rely on claims. We benchmark against reality.

Our Test Lab reruns real surveys and decision scenarios on Synthetic-People and compares them with human panel results across alignment, replication, and error margins — transparently and in public.

But perfect survey replication is not our goal. Business decisions are not made on surveys. They are made on customer understanding.

Not "How well does it replicate a survey?" But "How does it help you with customer understanding?"

No. And we are explicit about this.

Synthetic-People is a force multiplier, not a substitute for real humans.

We are strongest where speed, iteration, and early understanding matter: testing hypotheses, exploring scenarios, pressure-testing concepts before launch, refining research direction, surfacing emotional drivers and hidden objections.

Traditional research is critical for intense validation. But too slow to guide early direction. Synthetic-People give rapid behavioural signal — so by the time you invest in real customer studies, you are validating sharper, better-formed decisions with far higher confidence.

You should not blindly trust synthetic research when:

— The problem requires intense lived experience validation (e.g. medical, legal, trauma-heavy contexts) — Extremely weak signals or novel cultural nuance are involved

Our system says "Not enough signal" rather than manufacture certainty. Because false confidence is more dangerous than acknowledged uncertainty.

No. You do not need to share PII or sensitive customer data to generate insights.

Synthetic-People are grounded in billions of aggregated, ethically sourced customer action data, validated research studies, and neuroscience-informed inputs.

Your first-party data is optional. But when provided, it acts as an enrichment layer that sharpens contextual relevance. Any internal data remains strictly confidential, purpose-bound, and never used to train external or shared models.

Privacy is not a compliance layer added later. It is a core design constraint of the system.

Traditional human research is already shaped by bias — recall gaps, social desirability, and sampling constraints.

Rather than assuming bias disappears, we surface it, parameterise it, and make the behavioural reasoning traceable.

Instead of relying on a single averaged persona, Synthetic-People generate diverse behavioural profiles across segments, contexts, and decision environments. Variation is preserved, not flattened. Edge behaviours are examined, not averaged away.

Representation is treated as a spectrum of behaviour, not a simplified stereotype.

Yes. The platform is designed for holistic customer understanding, not isolated methods.

You can run qualitative explorations to uncover depth, motivations, and emotional drivers — and quantitative studies to detect patterns, distributions, and directional signals at scale.

Both modes support multi-turn interactions, allowing you to probe, iterate, and test unlimited "what-if" scenarios in a continuous research loop.

Depth and scale are not treated as trade-offs. They operate together to reveal how decisions form, not just what people report.