StressSense uses wearable technology and machine learning algorithms to gather and analyze physiological data such as heart rate variability, sleep patterns, and activity levels.

This data, combined with self-reported mood check-ins and workload assessments, provides a comprehensive picture of an employee's stress levels.

Through pattern recognition and anomaly detection, the platform predicts burnout risks and triggers alerts to HR departments, who can then deploy tailored interventions like flexible work arrangements, counseling services, or workload adjustments.

StressSense offers proactive solutions by predicting burnout before it fully manifests, reducing healthcare costs, improving employee satisfaction, and preserving organizational knowledge.

Unlike existing EAPs, it focuses on real-time monitoring and personalization, allowing tailored interventions that directly address individual needs.

The technology required for StressSense, including wearables and AI analytics, is mature and scalable.

Potential challenges include data privacy regulations and the need for integration with existing HR systems.

The market for workplace wellness solutions is growing, with few competitors offering real-time analytics.