Use Case 1
Annual Compensation Review
Trigger: Merit cycle planning begins
1.Pull market data for all roles and locations
2.Calculate compa-ratios across workforce
3.Identify employees below P25 or above P90
4.Generate market adjustment recommendations
5.Model budget impact of proposed changes
6.Present findings with competitive context
✓
Data-driven compensation adjustments that balance market competitiveness with budget constraints.
Use Case 2
New Role Pricing
Trigger: New position created
1.Identify comparable roles in market data
2.Adjust for location, industry, and company stage
3.Generate recommended pay band with min/mid/max
4.Compare to internal equity for similar roles
✓
New roles priced correctly from day one.
Integrations
Comp Data (Radford, Mercer, Pave, Levels.fyi)
HRIS (Workday, ADP)
Spreadsheet / Analytics Tool
Industries
healthcaretechnologyfinancial-servicesretailmanufacturingprofessional-servicespublic-sectorhospitalityother
See It In Action
A sample conversation with this agent
How does our software engineer compensation compare to market?
I'll pull benchmarking data for Software Engineers across all levels. For L4 (mid-level) in San Francisco: your median total comp is $185K vs. market P50 of $195K (5% below) and P75 of $225K (18% below). For L5 (senior): you're at $235K vs. P50 $240K (2% below). Your L3 (junior) comp is actually above P50 at $145K vs. $138K. The biggest risk is at L4 where you're below market median - this correlates with your higher attrition rate for mid-level engineers.
We're opening an Austin office. What should we pay?
Austin compensation data relative to your SF base: average cost-of-labor adjustment is 82-88% of SF rates, varying by role. For engineers, Austin P50 is 85% of SF. For sales, 90%. For G&A, 80%. I recommend location-based pay bands rather than flat adjustments. Want me to generate the full Austin pay band proposal with recommended ranges by level?