OVI is the AI-native ATS where you chat to hire. Two AI agents — Sora for sourcing and Milo for screening — find candidates, score every CV, and run AI interviews, all controlled from a single chat.

What does "chat to hire" mean?

Chat to hire is OVI's AI-native hiring workflow: instead of clicking through a traditional ATS, you describe what you need in chat and OVI's AI agents source candidates, screen applicants, run AI interviews, and send outreach for you.

What is an AI-native ATS?

An AI-native ATS is an applicant tracking system built around AI agents rather than forms and pipelines. OVI's AI chat ATS lets recruiters source, screen, interview, and follow up through conversation — the agents do the work and surface only the candidates worth meeting.

Who are Sora and Milo?

Sora is OVI's AI sourcing agent — she scans LinkedIn, the open web, and your ATS history to find best-fit candidates and reach out in your name. Milo is OVI's AI screening agent — he scores every applicant against your rubric and surfaces a ranked shortlist.

How do OVI's AI interviews work?

OVI runs async AI interviews for every shortlisted candidate and returns scores, transcripts, and recordings — so your team only meets the people worth meeting.

EU Pay Transparency Is Live. Your AI Pay Equity Tools Probably Can't Deliver.

By Chris Weinmann, Founder, OVI

Published 2026-07-04

What does the EU Pay Transparency Directive require?

Seven standardised gender pay gap metrics, mandatory joint pay assessment at 5% unexplained gap, pay range disclosure in job postings, and bans on salary history questions.

How do AI tools detect pay gaps?

Multivariate regression controls for legitimate factors to isolate the residual unexplained gap that may indicate systemic pay inequity.

Why were so few employers ready by the June 2026 deadline?

Data fragmentation: 68% of organisations run 6+ HR systems. AI tools need consolidated compensation data — which most organisations don't have.

Can AI pay equity tools make pay gaps worse?

Yes — AI trained on historical biased data can codify existing disparities rather than flagging them. Human-in-the-loop validation is essential.