AI-driven personalized nutrition platform backed by scientific research
ZOE pairs machine learning and biology research to deliver personalized nutrition guidance at scale. The stack—React Native, Python/FastAPI, dbt, BigQuery, and Braze—reveals a mobile-first consumer product with heavy data and personalization infrastructure. Active work across causal modeling, lifecycle campaigns, and subscription complexity suggests ZOE is shifting from single-feature retention (personalized recommendations) toward full-funnel monetization, with engineering capacity now stretched across payment integrations and platform scaling.
ZOE is a health-tech company delivering AI-powered personalized nutrition programs to tens of thousands of members in the US and UK. The product combines mobile apps (iOS/Android via React Native and Kotlin), backend data pipelines (Python, dbt, BigQuery), and behavioral AI to tailor dietary guidance to individual biology. The company was founded in 2017 by scientists from leading research institutions and operates as a remote-first organization of 51–200 employees. Revenue comes primarily from subscription memberships; current challenges center on payment-provider scaling, CRM platform utilization, and operationalizing research findings into product features.
Core stack: React Native and Kotlin for mobile, Python/FastAPI for backend, dbt and BigQuery for analytics, Braze for messaging, RevenueCat for subscriptions, Firebase for infrastructure. Also uses Mixpanel, AppsFlyer, and SQL. Currently adopting ElevenLabs.
Active projects include subscription management systems, causal modeling for user behavior, pricing experiments, payment provider integrations, lifecycle campaign automation (email, push, SMS, in-app), product experimentation platform, and infrastructure scaling for a global member base.
Other companies in the same industry, closest in size