The Synthetic Users
guide to supercharging
your User and Market
Research
Real world examples and use cases for Synthetic Users in research.
As AI-generated insights become faster and cheaper, the real challenge is no longer access — it's trust.
Synthetic Users are a blend of AI and real-world insights, crafting scenarios that resonate deeply and mimic real human experiences. The goal is always to make better decisions and decrease the time to insight.
We've moved beyond mimicking human behaviour to building decision-grade entities that reduce risk. Grounded in real user data, powered by multiple models to limit bias, and backed by traceable, auditable inputs — including your proprietary data — Synthetic Users enable reliable prediction and decision-making.
Synthetic Users has been mentioned in Science Magazine amongst many other publications.
Let's explore its distinct characteristics, the methods employed to verify its reliability, and the current as well as potential ways it's being utilized.
User and market research, throughout the product lifecycle.
Synthetic Users lets you run more research at a fraction of the time and cost of organic research. By combining synthetic participants with structured research workflows, you get continuous discovery instead of one-off studies — from early problem exploration to concept validation and usability testing.
We are not replacing organic users — just accelerating your research journey. You then decide how much organic research you need.
We have global coverage.
From New York to Mumbai, Synthetic Users cover urban, peri-urban, and rural audiences across regulated and consumer-facing sectors.
A multi-agent system where agents collaborate, negotiate, and learn from each other.
This architecture underpins the generation of diverse and insightful Synthetic User interactions, supporting nuanced data analysis and decision-making — instead of a single LLM with a single context window.
Compared to "you, using one LLM": more diversity in the samples, less anchoring in one context window, more synthetic / organic parity.
Synthetic Users vs Traditional Insights.
When applied in the right phase and product categories, Synthetic Users exhibit a remarkably high level of accuracy compared to conventional research methodologies. Read the independent parity study →
Accuracy of Synthetic Users — as rated by researchers
Four-parameter measurementThematic overlap
Depth and specificity of insights
Comprehensiveness of coverage
Qualitative alignment
Highest value sits in Need Identification, Concept Testing, Development, Growth, and Maturity.
This is an evolving scenario as we run more comparative studies.
The difference between using a single LLM and using Synthetic Users is noticeable.
Less diversity in the samples, more anchoring in one context window, less synthetic / organic parity. Where you sit on this spectrum determines how much your insight is worth.
Your org using one LLM
Single context window, single model, single voice. Convenient — but high anchoring, low diversity, no grounding in your proprietary data.
Synthetic Users with proprietary datasets and multiple LLMs
Bias is reduced as multiple models negotiate. Real-world psychographic data anchors the population. Diversity rises sharply.
Synthetic Users with proprietary datasets, multiple LLMs and RAG
Upload your sources. Now your synthetic population is grounded in your reality — the most defensible, decision-grade insight available.
An AI-driven proxy of your real-world user base.
Your team gets access to Synthetic Users that simulate your audience's behaviours, preferences, and pain points. Gather valuable insights with far greater speed — informing product development, marketing strategy, and operational decisions.
A single platform that every department can task with their own audience questions — same population, consistent grounding, traceable inputs.
Step-by-step tutorials and the science behind the platform.
Session 1 — Platform overview & first study
Workspace setup, your first project, and an exploratory study end-to-end. 28 min.
WatchSession 2 — Generative research
Open-ended questions, unexpected insights, and studies that surface what you didn't know to ask. 35 min.
WatchSession 3 — Evaluative research
Validate designs, test concepts, and measure usability with structured studies. 32 min.
WatchQuick UX testing tutorial
Upload your designs, define tasks, and watch Synthetic Users navigate your screens for usability feedback. 8 min.
WatchHow to get the most out of Synthetic Users.
A live working session with our founders and a special guest, plus an open Q&A.
Follow our YouTube channel for more tutorials.