dosync provides an adaptive supplement management platform that continuously adjusts dosing schedules based on users’ sleep patterns, training intensity, and stress levels. The system offers dynamic intelligence, context‑aware reminders, and a unified supplement library, allowing users to track daily intake and view a personalized dosync Score. By learning the body’s rhythm, dosync aims to simplify nutrition optimization without manual calculations.
Funding
Funding not disclosed
Founders
Product
Problem
Many individuals take multiple dietary supplements but struggle to maintain optimal timing and dosage, leading to reduced efficacy and potential over- or under-consumption. Traditional supplement tracking relies on static schedules that do not account for daily variations in sleep, exercise intensity, or stress levels.
Solution
dosync provides an intelligent supplement management platform that continuously learns a user’s physiological rhythms and adjusts dosing schedules accordingly. By integrating data on sleep patterns, training intensity, and stress, the system generates dynamic, context‑aware reminders to ensure each supplement is taken at the most effective time. Users can track daily intake, view a consolidated supplement library, and monitor progress through a personalized dosync Score. The platform emphasizes simplicity and trust, offering a seamless interface for adding supplements and reviewing adherence without manual calculations.
Target Audience
Primary users are health‑focused consumers, athletes, and wellness enthusiasts who manage multiple supplements and seek data‑driven dosing optimization.
Features
- Adaptive scheduling engine that modifies supplement timing based on real‑time sleep, activity, and stress inputs
- Context‑aware push notifications that prompt users to take specific supplements at optimal moments
- Unified supplement library for organizing all vitamins, minerals, and performance enhancers in one place
- Daily progress dashboard displaying intake compliance and a composite dosync Score
- Automatic learning algorithm that refines dosing recommendations as user data accumulates