Burnout does not happen overnight. It builds gradually through sustained overwork, inadequate recovery, and mounting workload pressure. By the time an employee reports feeling burnt out, the damage is already done. The Burnout Risk Intelligence Engine detects the digital footprint of burnout before employees reach that point.

The engine monitors 11 signals across four categories: work intensity (overtime, after-hours, weekend activity), focus and attention (app switching, low-focus blocks, schedule variability), recovery and breaks (break frequency and quality), and workload indicators (meeting load, email volume, engagement decline). Each signal is evaluated against the user’s own historical baseline, not a generic threshold.

This baseline comparison approach means the engine adapts to each person’s normal working pattern. A developer who routinely works late is not flagged simply for working late. They are flagged when their pattern changes, when they start working significantly more than their own norm, taking fewer breaks, or showing signs of declining engagement. Prior period comparisons further refine the score by identifying sustained trends rather than one-off anomalies.