NeuroDynamic Mapping
Real-time burnout risk and cognitive load monitoring using the Job Demands-Resources model with opt-in mood check-ins.
JD-R Burnout Risk Model
Input signals
- Opt-in mood check-ins (Slack/Teams)
- Meeting density metadata
- Response pattern data
- JD-R demand-resource ratio
- Recovery time signals
What RUDY surfaces
- Cognitive load index
- Burnout risk score
- Weekly team health digest
- Demand-resource imbalance alert
- Early intervention recommendation
Sample AI output
Burnout risk elevated for 3 engineers on Platform Squad: avg overtime hrs +40% over 6 weeks, context-switch rate 2.1× above baseline, 1:1 quality score dropped to 5.1/10. Recommend: workload rebalance and manager debrief.
How it works
Employees opt in to one-click check-ins
Team members complete optional one-click mood check-ins via Slack or Teams. Participation is always transparent, voluntary, and never passive.
JD-R model calculates demand vs. resource balance
RUDY maps check-in signals, meeting density, and response patterns to the JD-R model — surfacing demand-resource imbalance at the team level.
Manager receives weekly digest and alerts
A weekly team health digest surfaces burnout risk trends. Threshold alerts fire when the imbalance reaches intervention-worthy levels.
A day with RUDY
“Alex's team has had 3 cancelled sprints. RUDY's burnout risk score flags the team in the 'imbalance detected' zone — not any individual — and generates a workload reduction suggestion for Alex's next team meeting. Alex adjusts the sprint scope before anyone reaches a breaking point.”
Research foundation
Job Demands-Resources (JD-R) model by Bakker & Demerouti; burnout prevention research.
Frequently asked questions
See RUDY AI in action.
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No surveillance. No black-box scoring. Human review where it matters.