HumOS is a proactive health intelligence platform that consolidates data from wearables, lab results, nutrition, and sleep to generate a daily, prescriptive plan for training, diet, recovery, and supplementation. By continuously updating recommendations based on live biomarker inputs, it also enables users to run structured experiments on their routines and receive biomarker‑backed outcome reports.
Funding
Funding not disclosed
Founders
Product
Problem
Many health apps provide isolated data points from wearables, labs, nutrition logs, or sleep trackers, leaving users without clear guidance on how to act on this information. This fragmentation prevents individuals from making coordinated, data-driven decisions to improve their wellbeing.
Solution
HumOS is a health‑optimization platform that continuously aggregates data from bloodwork, wearables, nutrition, and sleep trackers into a unified system. Each morning the platform generates a single, prescriptive daily plan that outlines specific actions for training, nutrition, sleep, recovery, and supplementation, explaining the rationale behind each recommendation. By contextualizing biomarkers against personal baselines and goals, HumOS updates the plan automatically whenever new lab results or sensor data are received. The platform also enables users to run structured experiments—such as changes to diet, training, or sleep—and receive biomarker‑backed outcome reports, turning trial-and-error into measurable insight.
Target Audience
HumOS targets health‑focused individuals—such as biohackers, athletes, and wellness enthusiasts—who actively track multiple health metrics and seek personalized, data‑driven daily guidance.
Features
- Automated daily output that delivers a complete, actionable plan covering exercise, diet, sleep, recovery, and targeted supplements
- Integrated data pipeline that synchronizes wearables, lab results, nutrition logs, and sleep trackers into a single health profile
- Real‑time adjustment of recommendations based on newly uploaded bloodwork or sensor data, maintaining a closed feedback loop
- Cross‑domain insight engine that identifies relationships between metrics (e.g., low magnesium affecting sleep stages) and adapts recommendations accordingly
- Protocol Lab tool for designing and tracking structured personal experiments with biomarker‑based outcome reporting
- Continuous learning model that refines recommendations as more data layers are added and user responses are observed