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People, Benefits & Employment · Audits cleared · Hours back · sample run · 1 ms

EEOC Pay Equity Disparity Auditor

71,000.00 in unexplained annual compensation disparities identified across across 3 items.

Evidence · HRIS Employee Compensation Subledger
EMP-302,Senior Software Engineer,Engineering,L5,4.5,F,142000.00
Working

Female engineer has greater tenure (4.5 yrs vs 4.2 yrs) but earns $26,000.00 less (-15.5%) than male peer. Disparity exceeds 5% threshold.

Method

1Controlled for job band, functional department, and tenure in linear regression model.

2Calculated salary true-up adjustment required to eliminate statutory pay risk.

71,000.00
Total Discrepancy · Audited balance impact
ReferenceDescriptionSalary_Disparity
EMP-302Senior Software Engineer Wage Disparity26,000.00
EMP-305Principal Product Manager Wage Gap25,000.00
EMP-304Enterprise Account Executive Gap20,000.00
Reads

HRIS employee compensation records, plus federal and state equal pay audit parameters

Produces

Pay disparity risk heat map and legal remediation salary adjustment model (Resolution Dossier, Findings Schedule)

Price

Open EEOC Pay Equity Disparity Auditor on sample data now with no sign-up, then run your own files free for 14 days. $3,000 a month after that, cancel any time. 30% goes to the referring partner.

Your files

Read in memory for the session, never stored, never used to train a model.

Outputs are computed from your inputs and the tool's rules. Check them before you rely on them.

The offer

14 days on your own files, then $3,000 a month.

Open EEOC Pay Equity Disparity Auditor on sample data now with no sign-up, then run your own files free for 14 days. $3,000 a month after that, cancel any time.

On a sample file: 71,000.00 in unexplained annual compensation disparities identified across across 3 items.

Why not just use ChatGPT?

Rules that stay current: the rate cards, tariffs, code sets and regulations it checks against are kept up to date for you; a one-off prompt starts from nothing each time. Evidence that stands up: every finding cites the line, the file and the rule, so it holds up with a vendor, a payer or an auditor. The same answer every time: the checks are written rules, not a fresh guess, so this month's result can be compared with last month's. Nobody has to own it: no one inside has to build, test and maintain a home-made tool, and files are not pasted into a public chatbot.

Start on your own files

Your files are read in memory and never stored.

If you would rather talk it through, write to dev.sathya@baseloom.app; that reaches Dev Sathya, founder.