Science

Understand the science behind Synthetic Users and stay up to date with the latest papers

May 14, 2024

How we deal with bias

Harnessing the power of AI in our Synthetic Users, we strive for a balance between reflecting reality and ethical responsibility, ensuring diversity and fairness while maintaining realism.

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May 8, 2024

The Art of the Vibes Engine

Large language models (LLMs) like GPT-4 serve as powerful "vibes engines," empathizing with diverse groups and generating contextually relevant content. Their applications span market research, customer support, user experience design, and mental health support, offering invaluable insights and personalized experiences. While not infallible sources of truth, LLMs enable creativity, personalization, and connection within the realm of human language.

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May 8, 2024

Comparison studies. The opportunity lies in the deviation.

When we compare different studies, especially looking at what synthetic (artificial interviews) and organic (real-world interviews) data tell us, we often find they mostly talk about the same things but there's also a bit where they don't match up. This gap is super interesting because it's like finding hidden treasure in what we thought we knew versus what we might have missed.

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May 8, 2024

The transition to Continuous Insight

The transition towards Continuous Insight™ aligns research activities more closely with the dynamic needs of the business and ensures that product development is continuously informed by up-to-date user insights.

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May 14, 2024

There is a faster and more accurate way to do research. Use Synthetic Users.

How Synthetic Users is changing the research process.

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May 7, 2024

The wisdom of the silicon crowd

In the light of an ancient parable, we explore a new paper that dives into how ensembles of large language models match the prediction accuracy of human crowds. It reveals that combining machine predictions with human insights leads to the most robust forecasting results.

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March 28, 2024

What is RAG and why it’s important for Synthetic Research

Ahead of our RAG launch we explain Retrieval-Augmented Generation (RAG) and how it enhances Synthetic Users by providing increased realism, contextual depth, and adaptive learning, with profound implications for market research, user experience testing, training, education, and innovative product development.

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May 13, 2024

Synthetic Users system architecture (the simplified version).

Foundation models underpin Synthetic Users with advanced capabilities, enhanced by synthetic data and RAG layers for realism and business alignment, all within a collaborative multi-agent framework for richer interactions.

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March 5, 2024

Saturation score. How do we know how many interviews to run?

Determine your interview target for achieving topic saturation using our efficient approach, leveraging the historical wisdom of research pioneers. This method ensures deep insights with theoretical sampling at its core.

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March 1, 2024

Three research papers that helped us build ❤️ Synthetic Users

For the sceptics amongst us who need more tangible research in order to engage with this brave new world. Full disclosure: we are part of the sceptics.

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February 21, 2024

How Synthetic Users are gaining depth

Synthetic Users are evolving to address criticism about their generalist nature by incorporating representative data sets and personal narratives.

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February 20, 2024

How we compare interviews to ensure we improve our Synthetic Organic Parity

How do we know we are right? How do we know our Synthetic Users are as real as organic users? We compare.

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February 21, 2024

Synthetic Users: Merging Qualitative and Quantitative Research, in seconds.

At Synthetic users we are blurring the lines between qualitative and quantitative research. Here's how we are going about this transformative approach.

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