Slumber.ai provides an AI platform that ingests raw sleep data from wearables and home sensors, automatically stages sleep, detects apnea events, and generates personalized health reports. The system delivers real‑time feedback to users via a mobile app and offers clinicians API‑driven access to aggregated metrics, trend visualizations, and risk scores for early diagnosis and treatment adjustment.
Funding
Funding not disclosed
Founders
Product
Problem
Many individuals experience sleep disturbances, yet objective, continuous monitoring and personalized analysis are limited to clinical settings, leading to delayed diagnosis and suboptimal treatment. Healthcare providers lack scalable tools to aggregate and interpret large volumes of sleep data from consumer wearables and home devices.
Solution
Slumber.ai offers an artificial‑intelligence platform that ingests raw sleep signals from wearables, bedside sensors, and mobile apps, then applies deep‑learning models to automatically stage sleep, detect apnea events, and generate individualized sleep health reports. The platform delivers real‑time feedback to users via a mobile dashboard and provides clinicians with API‑driven access to aggregated metrics, trend visualizations, and risk scores. By automating data processing and interpretation, the solution enables earlier identification of sleep disorders and supports data‑driven treatment adjustments without requiring in‑clinic polysomnography for every patient.
Target Audience
Primary customers are sleep clinics, pulmonology and neurology practices, and health‑tech companies that need automated analysis of home‑collected sleep data, as well as tech‑savvy consumers seeking personalized sleep insights.
Features
- Multi‑modal data ingestion pipeline supporting actigraphy, heart‑rate variability, respiratory effort, and audio signals from consumer devices
- Proprietary convolutional‑recurrent neural network for automated sleep stage classification and event detection with clinically validated accuracy
- Personalized sleep score and actionable recommendations (e.g., sleep hygiene, CPAP titration) delivered through a native iOS/Android app
- Secure, HIPAA‑compliant cloud storage with end‑to‑end encryption and role‑based access controls
- RESTful API and FHIR‑compatible endpoints for integration with electronic health record (EHR) systems and tele‑medicine platforms
- Continuous model retraining using anonymized aggregate data to improve detection of emerging sleep patterns
- Dashboard for clinicians featuring cohort analytics, longitudinal trend graphs, and automated alerts for high‑risk patients