Agent StoreHuman ResourcesCompensation Analysis
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Pay Equity Analysis Agent

Human ResourcesCompensation Analysis

Runs statistical pay equity analysis across gender, race, and other protected classes to detect unexplained compensation gaps and support remediation planning.

4
Process steps
5
Integrations
2
Data inputs

Pay equity audits are typically conducted by outside consultants once a year or less, leaving compensation gaps undetected for long stretches and increasing legal exposure as the workforce grows and changes

Manually controlling for legitimate factors like tenure, performance, and role level while isolating potential bias requires statistical expertise most HR teams don't have in-house on an ongoing basis

The agent runs a regression-based pay equity analysis on a recurring basis, controlling for legitimate compensation factors such as role, tenure, performance rating, and location, to isolate any statistically significant unexplained gaps by protected class

It generates a remediation-ready report with specific affected employees and estimated correction costs, kept confidential to authorized compensation and legal reviewers

The agent pulls current compensation and employee demographic data from the HRIS, then runs a multivariate regression model controlling for legitimate pay factors including role level, tenure, performance rating, geography, and education where applicable. It isolates the residual variance attributable to protected class status and flags statistically significant gaps exceeding a defined threshold. Results are compiled into a confidential report identifying affected employee groups and estimated remediation cost, accessible only to designated compensation and legal reviewers.

1

Aggregate Compensation Data

  • Pull current compensation records from the HRIS
  • Pull employee demographic and protected class data under legal privilege
  • Gather legitimate pay factor data including role, tenure, and performance
  • Validate data completeness before analysis
Outcome: A complete, validated dataset is prepared for statistical analysis.
2

Run Regression Analysis

  • Control for legitimate compensation factors in the model
  • Isolate residual pay variance by protected class
  • Test statistical significance of identified gaps
  • Segment findings by department and role level
Outcome: Statistically significant unexplained pay gaps are identified.
3

Identify Affected Employees

  • Map statistical findings to specific affected employee groups
  • Calculate individual-level compensation gap estimates
  • Prioritize cases by gap magnitude and legal risk
  • Prepare confidential findings for authorized reviewers only
Outcome: Affected employees are identified with quantified gap estimates.
4

Support Remediation Planning

  • Calculate total estimated remediation cost
  • Model phased remediation budget scenarios
  • Prepare confidential report for legal and compensation review
  • Track remediation decisions for the next audit cycle
Outcome: Legal and compensation leadership receive a remediation-ready confidential report.
Workday or ADP
Sources compensation, tenure, and performance data
HRIS demographic modules
Provides protected class data under legal access controls
Statistical analysis platforms (R, Pytho
Secure document portals
Restricts confidential report access to authorized reviewers
Legal case management systems
Logs findings for privileged review tracking