Rey offers an AI‑driven health and fitness planning system that integrates data from wearables, calendars, and fitness apps to create daily action plans sized to a user’s current capacity. By calibrating chronotype, focus format, and recovery style, it identifies the primary constraint (e.g., sleep) and schedules two to three targeted actions directly into the user’s calendar, continuously recalibrating as life circumstances change.
Funding
Funding not disclosed
Founders
Product
Problem
Leaders and high‑performing individuals often try to adopt health and fitness habits without accounting for their existing workload, sleep patterns, and recovery capacity, leading to burnout and inconsistent progress.
Solution
Rey provides an adaptive health and fitness platform that continuously ingests data from wearables, calendars, and nutrition apps to assess a user’s chronotype, focus style, and recovery patterns. The system identifies the primary constraint—typically sleep—and generates two to three micro‑workouts or recovery actions sized to the user’s current capacity. These actions are automatically placed into the user’s calendar during optimal energy windows. As weekly demands shift, the platform recalibrates the plan, ensuring continuity without requiring a complete restart.
Target Audience
Rey targets busy professionals, entrepreneurs, and leaders who need a sustainable, data‑driven approach to health and fitness without disrupting their existing schedules.
Features
- Automatic data integration from Apple Health, Google Calendar, Hevy, MacroFactor, and other wearables
- Calibration engine that determines chronotype, focus format, recovery style, and accountability streaks
- Diagnostic scoring of nine attributes to pinpoint the primary constraint (e.g., sleep, focus, discipline)
- Daily schedule generation of 2‑3 micro‑workouts or recovery tasks, inserted directly into the user’s calendar at optimal times
- Continuous weekly recalibration that adjusts the plan as workload and recovery metrics change
- No replacement of existing tools; the platform acts as a decision layer above the user’s current stack