Maya approaches priors through the lens of pattern recognition. With a background in cognitive psychology and behavioral science, she helps users uncover recurring dynamics in their friendships whether subtle routines or long-term trends. Maya excels at guiding people to translate their intuitions into structured prior beliefs, rooted in observable behavior over time.

Julian takes a numbers-first, systems-thinking approach. Trained in applied statistics and decision theory, he works with users to construct priors from the ground up—clarifying assumptions, stress-testing beliefs, and bringing mathematical rigor to everyday social predictions. If it can be modeled, Julian can make it make sense.

Hannah helps users define priors by starting with narratives. With a background in sociology and user research, she invites users to tell stories about their friends’ habits, histories, and quirks. Hannah draws out the implicit beliefs hiding in plain sight. Her process is deeply human, weaving emotional nuance into predictive structure.