Pay Equity

How you justify pay, in writing, is the evidence.

"We pay him more because he is the breadwinner and she does not need as much." A compensation email like that is a pay-discrimination claim waiting to happen. VerbaPulse flags it before it sends.

Book a demo →See pricing

The problem

The justification is what gets tested

Pay decisions are defensible when they rest on role, skills, and performance. The risk is the email that justifies a gap on something else: he is the breadwinner, she will not negotiate, she does not need as much. Those reasons, in writing, map a pay difference onto a protected characteristic, which is exactly what a pay-equity claim or audit looks for.

The manager is being pragmatic about budget. But the wording turns a defensible decision into discriminatory evidence.

What it catches

The biased justification, rebalanced

VerbaPulse reads the draft as it forms and flags the span that creates the exposure, with a plain reason. Real output from the product:

VerbaPulseHigh

"We pay him more because he is the breadwinner and she does not really need as much."

Gender bias in a pay decision

Remove this reasoning, and review the decisionRemove

VerbaPulse flags the biased justification for removal. It does not offer a more defensible way to phrase it: rewording a pay gap that rests on a protected characteristic only manufactures a pretext. The reasoning comes out and a person reviews the decision.

The same check covers the related patterns:

Gendered pay justifications: "breadwinner", "she does not need as much".
Assumptions about who will negotiate: "she will not push back".
Pay tied to a protected characteristic rather than role or performance.
"Keep her offer low" reasoning.
Your own rules: upload your compensation policy and VerbaPulse enforces it on every draft.

How it works

In the inbox your team already uses

Install in minutes

The Outlook add-in deploys org-wide through the Microsoft 365 admin center, and the Chrome extension covers Gmail. No new tab, no change to how the team works.

Write as usual

As the draft forms, a risky span is flagged with severity and a plain reason, so the writer sees the exposure in the line they just wrote.

Fix before it sends

Apply the safer wording, or remove the line, while the message is still a draft. Anonymized events feed an audit trail your team can show.

Where this fits

A writing-time check, not your comp system

VerbaPulse does not run your compensation framework or your pay-equity audit. It catches the biased justification in the draft, while the wording can still change, so a defensible pay decision is not recorded with a discriminatory reason. It is one control inside email compliance for HR and recruitment, and it complements the systems you already run.

Email content is never stored: drafts are analyzed in memory and discarded immediately.
Nothing is used to train AI models.
The audit trail is anonymized by design: risk types, severities, and outcomes, never message text or named individuals.
It keeps a legitimate, role-based pay rationale intact while flagging the biased one.

For the evidence behind this, our language risk benchmark runs real, anonymized cases through the product and reports what it flags.

FAQ

Common questions

What pay language is risky in email?

A pay difference justified on a protected characteristic rather than role, skills, or performance: "he is the breadwinner", "she does not need as much", or assumptions about who will negotiate. Written down, that maps a pay gap onto a protected characteristic. This page describes language risk and is not legal advice.

How does VerbaPulse help?

It flags the biased justification as it is written and offers a role-based rewrite. For example, "he is the breadwinner and she does not need as much" is flagged as gender bias in pay justification, with rewrites like "his role requires more responsibility".

Does it set or change pay?

No. It does not run compensation or make pay decisions. It only checks the outbound email for the way a pay decision is justified.

Is email content stored anywhere?

No. Drafts are analyzed in memory and discarded immediately. They are never stored and never used to train AI models. The audit trail keeps anonymized risk events (type, severity, action taken), never message text and never named individuals.