Discrimination

Bias shows up in the words, not the policy.

"We should hire someone younger, she will probably start a family soon." A throwaway line in a hiring thread is the evidence in a discrimination claim. VerbaPulse flags it before the email sends.

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The problem

The policy is clean, the email is not

Every HR team has an equal-opportunity policy. The risk is not the policy, it is the candid internal email: a reference to someone being too old, an assumption about a woman starting a family, a shorthand about who will fit. These lines are written quickly, between busy people, and they are discoverable in a claim.

The writer is not trying to discriminate. They are thinking out loud about a decision. But the words attach a protected characteristic to that decision, and that is exactly what a tribunal looks for.

What it catches

Protected-characteristic language, at the moment of writing

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 should hire someone younger for this role, she is probably going to start a family soon."

Discriminatory reasoning: age and family status

Remove the reasoning, and have a person review the decisionRemove

VerbaPulse flags both biased spans here, the age reference and the family-status assumption, and marks the discriminatory reasoning for removal. For discrimination the fix is the decision, not the wording: the reasoning comes out and a person reviews it. Softening the phrasing does not make a biased decision lawful.

The same check covers the related patterns:

Age references: "someone younger", "too old", "fresh out of school".
Gender and family-status assumptions: "she will start a family", "he has young kids".
Proxies for race, nationality, or disability dressed up as preferences.
"Culture fit" used as a stand-in for a protected characteristic.
Your own rules: upload your equal-opportunity 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 the hiring decision

VerbaPulse does not make hiring decisions or replace HR judgment. It catches the biased phrasing in the draft, while the wording can still change, so a protected characteristic does not get attached to a decision in writing. 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 tells the difference between biased reasoning and a neutral one, so a fair note about a candidate is not flagged.

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 discrimination language does VerbaPulse catch?

Language that ties a people decision to a protected characteristic: age references, gender or family-status assumptions, and proxies for race, nationality, or disability, including "culture fit" when it stands in for one of these. This page describes language risk and is not legal advice.

How does VerbaPulse help?

It flags the biased span as it is written and, where the wording can be neutralized, offers a rewrite. For example, "someone younger" is flagged as age bias with rewrites like "a new team member", and a family-status assumption is flagged for removal.

Does it make or block hiring decisions?

No. It does not decide who to hire and it does not block anything. It only checks the outbound email for language that attaches a protected characteristic to a decision.

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.