MatrAIx is an open-source framework that generates and runs one million synthetic personas as LLM agents to evaluate AI systems across surveys, chatbots, websites, and native apps. It uses a 1,290-dimensional persona schema combining synthetic generation with human grounding to test products at population scale before real-world deployment. The tool includes a visual playground, CLI, and a public dataset released on Hugging Face.

• MatrAIx simulates 1 million synthetic personas as LLM agents to test products before real-world deployment, using a 1,290-dimension persona schema spanning background, psychology, capability, and behavior
• It tests across four environments—surveys, AI chatbots, web interfaces, and native apps (iOS, macOS, Android)—via a Playground GUI or CLI
• The persona dataset is publicly released on Hugging Face, synthetically generated but grounded in real human data, and the framework is MIT-licensed
• It's explicitly positioned as a sandbox for hypothesis generation and edge-case discovery, not a replacement for real user research

Researchers built MatrAIx, a platform that uses 8.3 billion simulated personas to evaluate how AI systems and digital products perform across diverse user types. The system combines human-grounded personas (extracted from Wikipedia, Stack Overflow, surveys) with synthetically generated ones, then runs them through four types of test environments—surveys, chatbots, websites, and apps—to measure how different user groups interact with and respond to products. In validation tests, the simulated personas behaved consistently with their assigned attributes 91.5% of the time, showing this approach can catch user-specific friction points and failure modes faster and cheaper than traditional human testing.

• MatrAIx uses 8.3 billion simulated personas (with a curated ~1M subset, 600K human-grounded/400K synthetic) to test products instead of recruiting real human testers
• In validation trials, synthetic personas correctly expressed or suppressed assigned behavioral traits 91.5% of the time across ten attributes
• The system ran 18,189 trials across eight tasks, revealing realistic behavioral variation like differing tolerance for price hikes, AI failures, and slow response times across user types