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

MatrAIx is an evaluation platform that uses 8.3 billion AI-powered persona agents to test how AI systems and digital products perform with diverse user types. The system includes a dataset of 1 million personas (half human-grounded, half synthetic), four interactive environments (survey, chatbot, web, app), and over 1,000 tasks across 25 domains. Testing showed the personas accurately express behavioral attributes 91.5% of the time, capturing real variation in user preferences like price sensitivity and failure tolerance.

• MatrAIx uses 8.3 billion simulated personas (built from ~1 million curated profiles) to test AI products instead of relying on expensive, slow human testing.
• Across 18,189 trials using three LLMs as persona "brains," the personas stuck to their declared traits (like price sensitivity or failure tolerance) 91.5% of the time.
• The system is designed to expose how different user segments react differently to the same product, countering the common practice of testing against a flattened "average user."