Simulate your customers with AI agents
Companies lack a scalable, reproducible way to test pricing, product, and marketing decisions with customer feedback before launching changes.
An open-source library and no-code web app that creates AI agent customer personas to run LLM-powered interviews and surveys with caching, reproducibility, and optional human validation.
Product, marketing, and research teams at companies who need to simulate and validate customer responses at scale.
Prior to building E[P] I was as founding member of Uber's Legal Data team and spent 7 years managing cross-functional data reporting. Before joining Uber I was a tax and investment funds attorney at Ropes & Gray LLP and served as an Assistant Attorney General in Massachusetts prosecuting environmental and white collar crimes. Before law school I managed simulations contracts for Northrop Grumman. Harvard JD & Columbia BA (Math). Harvard JD, Columbia BA Math.
Co-Founder of Expected Parrot. Professor at MIT Sloan. Economist. Former Army Officer.




