Why we built the Intelligent Selection Platform

For a long time, Recright was known as a video screening tool.  

That made sense. CVs are a weak signal, and video helped hiring teams see more of the person behind the application. It added humanity early in the process. For many teams, it was a real improvement.

But over time, something became obvious to us. Better input didn’t reliably lead to better hiring decisions. Video helped teams see candidates more clearly, but it didn’t help them decide more clearly. The same problems kept showing up later in the process: vague criteria, inconsistent interviews, and decisions that were hard to explain.  

That’s where hiring usually breaks.

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Video screening solved only part of the problem

Video screening improved the screening moment itself, but it rarely changed what happened next. Candidates were assessed on video, then evaluated later using different logic, different questions, and different standards. What was learned early on didn’t consistently carry through the rest of the process.  

Without structure, screening becomes a snapshot instead of part of a coherent decision journey – echoing what McKinsey describes when it urges companies to move from traditional CV screening to skills-based, system-level talent decisions and to build a repeatable “hiring engine” rather than isolated decisions.

In practice, video helped teams see more, but it didn’t help them decide better.

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Gut-driven hiring still dominated

Most hiring decisions don’t fail because people don’t care. They fail because the process isn’t strong enough to support good judgment.

Even when teams collect rich information, decisions are often made in loosely structured ways. One interviewer focuses on culture, another on experience. One hiring manager probes deeply, another improvises. When it’s time to choose, opinions outweigh evidence.

Gut feeling quietly takes over. Not because it’s preferred, but because there’s nothing better to lean on.

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Enterprise challenges demand stronger solutions

As organizations grow, this problem becomes harder to ignore.

Talent acquisition leaders are expected to stand behind hiring decisions with confidence – legally, ethically, and operationally. They need processes that are fair, consistent, and defensible across teams and regions. At the same time, hiring managers are busy. They’re not recruitment experts, and they shouldn’t have to be.

What’s missing is practical support.

Most teams already know that structured, competency-based selection works. The problem is getting it to happen consistently in real interviews, with real people, under real time pressure. This mirrors what McKinsey sees when organisations treat hiring as a repeatable “engine” with clear performance criteria and feedback loops, rather than a series of one-off decisions.

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What years of real-world use taught us

Over the years, Recright has been used by over 14.000 recruiters and hiring managers, across +500 organizations, in 180 countries worldwide.

Across industries, roles, and cultures, the pattern was remarkably consistent. Video made recruitment more human. But whether hiring decisions actually improved often depended on something else: structure.

Teams that paired video with clear criteria, shared interview logic, and consistent evaluation made better decisions. Teams that didn’t ended up with more data, but still some uncertainty.

Seeing that pattern repeat across customers, and use cases is what pushed us beyond video.

Hiring is becoming more evidence-driven

There’s a clear shift toward evidence-based hiring driven by the need to make better decisions at scale. But knowing what works isn’t the same as doing it consistently.

Best practices often live in guidelines and training decks, not in the interview itself. Without system support, structure erodes under time pressure, and good intentions aren’t enough.

At the same time, major AI players are starting to build the infrastructure for more skills- and evidence-based matching at scale – for example, OpenAI’s “Expanding economic opportunity with AI” outlines a jobs platform and certifications designed to connect employers with AI‑fluent talent based on proven capabilities rather than just traditional CVs.

Evidence-based hiring isn’t blocked by mindset. It’s blocked by execution.

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Progress in ai should be accessible for hr

AI will play a role in closing that execution gap but only if it’s applied carefully.  

For us at Recright, “intelligence” means supporting human judgment: clarifying criteria, guiding interviews, and helping teams turn conversations into comparable evidence.  

And it has to be done responsibly. Transparency, trust, and compliance, including with the EU AI Act’s rules on high-risk HR and recruitment systems, aren’t optional. 

Intelligence only helps if people can rely on it.

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Part of an ecosystem, not a replacement

Along the way, another pattern became clear.

ATSs are excellent at managing workflow, compliance, and process logistics. But they are intentionally neutral when it comes to the quality of the interaction between recruiter, hiring manager, and candidate. 

And that’s the space we focus on. 

You can see this split in how companies like Google run structured interviews as a distinct layer on top of their internal systems: banked questions, scoring rubrics, and calibrated feedback designed to improve decision quality. You can read about it in Google’s re:Work guide “Use structured interviewing”.

Recright doesn’t replace your ATS. We complement it by strengthening the selection decisions that sit on top of it. What started as screening naturally expanded into interviews and decision support, because hiring quality lives across the whole decision journey.

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We are supporting a larger vision

Our Intelligent Selection Platform is the result of this evolution.

Not intelligence as hype. Not AI for its own sake. But a system designed to make structured, fair, evidence-based hiring possible in everyday work.

This shift is about raising the standard for how hiring decisions are made in practice. We want to help teams move from snapshots to evidence, from intuition to structure, and from inconsistent decisions to confident ones.

Better hiring doesn’t come from stronger opinions.

It comes from building a repeatable selection engine that replaces guesswork with evidence and scales good decisions across the organization.