Let AI find your perfect matches

Predictive hiring uses past outcomes to estimate how likely a candidate is to succeed in a role, instead of relying on how a CV reads. Mainder scores every candidate against your role requirements, your company values and historical success patterns, and becomes more accurate with each placement because it learns from your hiring decisions and client feedback.

Quality of hire can only be confirmed after the fact, by whether the person stays and performs, so the useful work is connecting that outcome back to the signals that predicted it. Mainder keeps the score, the criteria and the decision attached to each candidate, so you can compare the shortlist you approved against how those hires actually turned out and correct the criteria instead of arguing from anecdotes.

It removes one specific source of bias: the inconsistency of evaluating candidates differently depending on who reviews them and when. Mainder scores every profile against the same explicit criteria, the role requirements and your company values, and records why each score was given, so the reasoning is auditable. It does not replace human judgement or your own fairness policy, it makes the criteria visible enough to be challenged.

By ranking before you read. Mainder returns match percentages that surface the strongest candidates already in your database before anyone reviews them manually, which cuts time to shortlist by around four times and stops strong profiles from staying buried under volume.