“Magic AI Scores” Won’t Fix Your Recruiting—What to Do Instead

Introduction

A resume. A job description. A black-box AI that instantly spits out a score.

Sounds efficient, right?

But then you’re staring at a list of scores wondering:

• “Why did this person get a 92?”
• “Why is this one a 47 if they look pretty good?”
• “What if I’m missing out on a great candidate just because a machine said so?”

You’re not alone. Many AI-powered recruiting tools promise scoring magic, but leave recruiters more confused than confident. The truth is, if you’re not involved in defining what “good” looks like, you’ll always feel disconnected from the outcome.

That’s exactly the problem we set out to solve at Brainner.

Why Most AI Scoring Models Fall Short

AI-powered scoring systems often analyze resumes against job descriptions with little to no recruiter input. This creates three core issues:

• Lack of context – AI can’t read between the lines. Maybe the JD includes nice-to-haves that aren’t real deal-breakers. Or maybe key requirements like “must have worked at a competitor” weren’t listed at all.
• No control over what matters most – All requirements are treated equally, even if some are critical (e.g., Python experience) and others are minor (e.g., exposure to Agile).
• No transparency – You don’t know why a candidate scored a 70 or an 85. You just get the number and have to trust it blindly.

As a result, many recruiters end up second-guessing the system—or worse, ignoring top talent who didn’t score high enough on an arbitrary formula.

Brainner: A Human + AI Approach That Keeps You in Control

At Brainner, we believe AI should assist human decision-making—not replace it. That’s why we built a collaborative model:

• 🧠 You define the criteria.

• 🤖 Brainner analyzes resumes against it—instantly and objectively.

This way, you keep the human expertise, and AI handles the grunt work.

Step 1: Define the Right Criteria (Automatically, Then Customize)

When a new role opens, Brainner pulls the job description directly from your ATS and suggests a set of screening criteria—mapped to skills, experience, education, and more.

But here’s the key: you’re not stuck with those suggestions.

• Want to add something not in the JD, like tenure or no career gaps? Go ahead.
• Want to remove a requirement that was just marketing fluff? Delete it.
• Want to prioritize candidates from certain industries or competitors? Add it.

You’re in control from day one.

Step 2: Weight Each Requirement According to Its Importance

In most scoring models, every requirement is treated the same. That’s not how recruiting works.

In Brainner, you can assign different weights to each criterion based on what’s actually important for this role:

• Reduce the impact of a secondary tool your team can easily train for.
• Prioritize must-haves like years of experience or specific industries.

This ensures that Brainner ranks candidates based on your priorities, not a generic formula.

Step 3: Let AI Do the Heavy Lifting—at 100x Speed

Once your criteria and weights are set, Brainner takes over.

Our AI model—trained daily on how to better detect gaps, measure tenure, and even assess traits like communication—evaluates each candidate instantly.

It doesn’t tell you who to hire. It tells you who meets your criteria—and who doesn’t.

From there, you can:

• Instantly see a ranked list of candidates based on alignment
• Filter by who meets mandatory requirements
• Dive into gaps, reasons for rejections, and strengths per profile

No guessing. No blind trust. Just real-time, explainable insights.

Conclusion: AI Should Empower, Not Confuse

If you’ve been burned by “magic scores” in recruiting, you’re not alone.

The solution isn’t more black-box AI—it’s giving recruiters the tools to define their own success criteria, and letting AI work within that framework.

That’s what Brainner does.

✨ You define what matters.

⚡️ AI analyzes at scale.

💡 You make better, faster, data-driven decisions.