Feedback & Calibration System
Closed-loop learning from every manager action — RFCS tracks outcomes and recalibrates models to improve future recommendation accuracy.
Closed-Loop Learning Cycle
Input signals
- Manager action completions
- Recommendation acceptance/rejection
- Team outcome data
- Employee outcome tracking
- Model accuracy feedback
What RUDY surfaces
- Outcome attribution report
- Closed-loop learning cycle
- Model calibration timeline
- Tenant-specific improvement data
- HR accuracy dashboard
Sample AI output
Calibration drift detected: Manager A rates team 12% higher than cross-functional peers for equivalent output. Suggested action: share anonymized peer benchmarks before next review cycle to reduce grade inflation.
How it works
Manager takes action on RUDY suggestion
When a manager acts on a RUDY recommendation — adjusting workload, scheduling a coaching conversation, or re-pairing a team — RFCS begins tracking the outcome.
RFCS tracks outcome and records result
Over 4–8 weeks, RFCS monitors team health signals, engagement indicators, and follow-up assessment data to attribute outcomes to prior recommendations.
Model recalibrates from real-world outcomes
Outcome data feeds back into the recommendation models, recalibrating confidence weights for similar future signals and improving accuracy over time.
A day with RUDY
“After adjusting Maya's workload based on RUDY's suggestion, her burnout risk score drops from 87 to 42 over 6 weeks. RFCS logs this as a successful intervention and increases confidence weights for similar workload signals in the future. RUDY becomes more accurate the more it is used.”
Research foundation
Bayesian updating, online learning systems, and outcome attribution in organizational AI.
Frequently asked questions
See RUDY AI in action.
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No surveillance. No black-box scoring. Human review where it matters.