Brandty improves operational efficiency by 45% with Mainder

Hiring for values takes time, and there is no shortcut for the part that matters. Knowing whether someone shares the way a team works takes a real conversation, sometimes two. What you can shorten is the road to that conversation.

At companies that hire this way, the first screen is often done by the same person who later interviews, quite often someone in leadership with plenty of other things to do. They read CVs one by one, check the basic requirements and set aside whoever does not fit. It is mechanical work, and it is the work that eats the most hours.

There is a second, less visible problem. When the screen depends on the day and on who does it, the criteria drift. A candidate who would have passed on Monday does not pass on Friday afternoon. For a company that hires for values, that inconsistency weighs.

AI Scoring scores each candidate on three things: your company's values, the requirements of the role and the patterns in your past hires. You define what counts. The AI applies it the same way to the first candidate and the hundredth.

The result is a match percentage for each candidate. You open the role and see the strongest fits at the top, with the basics already checked, instead of an unsorted pile of CVs.

Every score is recorded with its reasoning. If one surprises you, you can see which criteria produced it and correct them, and the reasoning stays visible to whoever needs to review it.

With every hire you close, the scoring learns from your decisions and sharpens its predictions. The final call is still yours. The score only orders the list.

The first step is to write the company's values down as concrete criteria: what behavior you are looking for, what experience shows each one, what is essential and what is nice to have. That exercise, which many companies have never done in writing, is what AI Scoring then uses to score.

Once it is done, the person who interviews stops screening. They arrive at the interview with the mechanical part settled and spend the conversation on what the AI cannot measure: how the candidate thinks, how they work with others and whether they want to be there.

Over time, each candidate's score, criteria and decision stay together. That lets you look back, compare who was prioritized with how those hires turned out, and adjust the criteria with data instead of anecdotes.

One question worth leaving open: how much weight should the score carry against the interviewer's instinct? There is no single answer, and each team finds its own by hiring.