For leadership, hr and compliance
Make staffing decisions you can audit and defend
Keep a full trail of every recommendation, test recommendation rates for fairness, and never use protected attributes.
The problem
AI hiring and staffing tools are hard to trust when nobody can see why they suggested someone, or whether they treat groups differently.
The result
Answers for auditors, works councils and your own leadership.
How FitRank does it
- 1Protected attributes such as gender, age, religion and caste are never stored, sent to AI or used as signals.
- 2Every recommendation records the model, versions, probabilities and what the manager did, in an exportable audit CSV.
- 3The fairness view tests recommendation rates by location and practice with the four-fifths rule.
The features that do the work
Health and fairness dashboardsSee how often managers agree with the shortlist, override rates by week and model, real placement outcomes, and recommendation rates tested for fairness.
Roles, sign-in and auditAdmin, resource manager, HR and viewer roles, hashed passwords and short-lived tokens, row-level security per company, and an audit CSV of every decision.
Thresholds and model versionsSet your own shortlist and review cut-offs, compare model versions on unseen tasks, and switch or roll back in one click. Every change is versioned.
More use cases
Be one of the first teams to staff with FitRank
We're taking on a small number of pilot companies. Pre-register for early access, or talk to us about an annual licence.