Process
This is how our AI works and achieves unparalleled accuracy. Every step ensures that collected data is not only analyzed but also compared against past insights and refined through human-driven adjustments. Trained on renowned studies, books, and research, our model continuously learns and evolves, drawing from thousands of carefully curated resources.
Training
Training the AI with Trusted Knowledge
Our AI is trained using thousands of renowned studies, books, and research papers on stress, burnout, HRV (Heart Rate Variability), and sleep patterns. We use Natural Language Processing (NLP) models to extract key insights from these texts, allowing the AI to develop a deep understanding of stress factors, physiological markers, and risk patterns. Through reinforcement learning and fine-tuning, the model continuously improves, ensuring it aligns with the latest scientific findings.
collect
Data Collection & Preprocessing
Once wearable devices record raw physiological data—such as HRV fluctuations, sleep cycles, activity levels, and resting heart rate—our system cleans, normalizes, and structures this data for analysis. Using signal processing techniques and statistical methods, we remove noise and anomalies, ensuring accuracy. The AI then correlates this data with survey responses to create a richer, more holistic dataset.
Analysis
Stress Scoring & Pattern Recognition
Our AI evaluates stress levels by comparing real-time physiological data with historical trends, industry benchmarks, and research-backed thresholds. It uses machine learning models, such as decision trees and neural networks, to assign a stress level score from 0 to 100, where higher scores indicate an increased risk of burnout. By analyzing patterns—such as declining HRV, inconsistent sleep, or sudden changes in activity levels—the AI detects early warning signs of chronic stress. To ensure even greater accuracy, human experts review and refine the AI-generated insights, adding context and adjusting for individual variability, creating a hybrid approach that enhances precision and reliability.
refinement
Continuous Learning & Refinement
Each new dataset helps the AI refine its accuracy. By leveraging time-series analysis and anomaly detection, it improves future predictions and adapts to individual employee baselines. Additionally, human experts validate AI-generated insights, correcting potential misinterpretations. This hybrid approach ensures that the model doesn’t just rely on automation but evolves with real-world experience, becoming increasingly precise over time. To protect privacy, all data is encrypted, and personal or sensitive employee details are completely removed. Only anonymized raw data is retained, ensuring that no information can be linked to an individual while still allowing for accurate trend analysis.
Support
