Use Case 1
Pre-Calibration Data Preparation
Trigger: Calibration session scheduled
1.Aggregate all ratings by team, level, and demographics
2.Generate rating distribution charts
3.Identify statistical outliers and inconsistencies
4.Prepare 9-box talent grid
5.Flag succession gaps for critical roles
6.Create discussion guides for flagged employees
✓
Calibration session starts with data-driven insights instead of opinion battles.
Use Case 2
Bias Detection in Ratings
Trigger: Post-rating submission, pre-calibration
1.Analyze ratings by demographic dimensions (with privacy controls)
2.Compare to performance data (goals met, project outcomes)
3.Identify patterns suggesting bias (consistently lower ratings for certain groups)
4.Generate confidential report for HRBP
5.Recommend calibration adjustments
✓
Equitable rating outcomes with bias patterns surfaced proactively.
Integrations
Performance Platform (Lattice, Culture Amp)
HRIS (Workday)
Analytics / BI Tool
Presentation Tool (Google Slides)
Industries
healthcaretechnologyfinancial-servicesretailmanufacturingprofessional-servicespublic-sectorhospitalityother
See It In Action
A sample conversation with this agent
Prepare the calibration materials for the engineering org Q4 talent review.
I'll generate the calibration package for Engineering (142 employees, 12 managers). The package includes: 9-box talent grid with current placements, rating distribution by team (highlighting teams skewing high or low vs. org norm), succession pipeline status for critical roles, year-over-year rating movement analysis, and specific flags where ratings appear inconsistent with performance data. I'll also prepare discussion guides for the 15 employees whose ratings I flagged for calibration discussion.
Show me which managers might be inflating ratings.
Comparing rating distributions across your 12 engineering managers: 3 managers have distributions significantly above the org median. Manager A rated 80% of their team 'Exceeds' (org average: 25%). Manager B has zero 'Meets Expectations' ratings. Manager C's team ratings increased an average of 0.8 points this cycle with no corresponding project outcome changes. I recommend these three teams receive focused calibration review. Want me to generate the specific comparison data for the calibration session?