We believe that for synthetic data to be a positive force for market growth and public understanding, it must be transparent, accountable, and honest.
Our entire system is designed around the ethical frameworks established by industry leaders like ESOMAR. We don't just provide data; we provide data you can trust.
Our service is designed to "augment and predict". We create high-quality synthetic cohorts to simulate survey responses at scale.
This allows you to achieve the impossible: test hundreds of concepts, reach hyper-niche audiences, and get statistically significant quantitative data in hours, not months. We augment your decision-making capabilities.
Our system is built on the fusion of multiple data types: categorical (demographics), continuous (scales), and textual (social media data). Our Persona DNA architecture models complex relationships between these data types, ensuring AI agents can handle any standard survey question.
We do not require any data from you other than your survey content and audience definitions.
We use a proprietary hybrid approach of Deep Generative Modeling and Agent-Based Simulation. Unlike simple look-alike modeling which can amplify bias, our approach is a three-step process:
Our system's strength comes from the vast, diverse, and continuously updated information integrated into our Persona DNA (including public census data, licensed third-party research, and anonymized web data).
We do not use your proprietary survey questions or results to train our core models for other clients. Your project-specific inputs are isolated and used solely for executing your project.
We are committed to maintaining the highest standards of data governance and security.
All data used to train our models is ethically sourced. We employ a robust information security framework (modeled after ISO 27001) and conduct regular vulnerability assessments to ensure our systems are resilient against attacks.
Our service is inherently privacy-preserving. We provide insights based on synthetic panels without collecting Personally Identifiable Information (PII) from real individuals for your specific survey. This eliminates risks associated with GDPR, CCPA, and other data protection laws. Furthermore, to optimize our global service response, when you proactively submit a booking or contact form, we collect your approximate geographic location (country and city only) via your IP address. This data is used solely for internal regional support and is never used for precise tracking.
We are "AI-Empowered Experts", not "AI Replacements". Every project is set up and reviewed by human research professionals. Our ethical review process ensures our technology is applied responsibly and the generated insights are sound and valid.
No technology is a silver bullet. As your expert partner, we believe in being upfront about the known challenges in the synthetic data field.
Without a foundation of massive, diverse, and high-quality real-world data, it is difficult (perhaps impossible) to create high-quality synthetic data or cohorts using LLMs alone. The principle of "Garbage In, Garbage Out" applies.
This is why our "Persona DNA" is built on a fusion of multiple reliable data sources.
If the industry over-relies on synthetic data without refreshing models with new real-world data, we risk creating a feedback loop where AI learns only from other AI-generated content. This leads to compounding inaccuracies.
We mitigate this by continuously integrating new real-world data signals into our models.
Many synthetic data methods tend to underestimate fringe opinions, resulting in a narrower range of views compared to real data (e.g., smaller standard deviations).
Our reasoning engine includes specific parameters to model and preserve a realistic spectrum of opinions, including contrarian views.
Evidence suggests that purely synthetic data may lack the empathy and "messiness" of real human responses, leaning more towards "logical" answers.
Our "Persona DNA" model explicitly incorporates psychological traits, cultural values, and personality frameworks (like Big Five) to generate responses that are not just logically consistent, but characteristically consistent.