Recruitment has spent years automating the wrong things: spam, noise, and lazy filtering. In this interview, Daniel Stoica, Founder & CEO of RMT Labs, explains why black‐box hiring tech still wastes time, why it is dangerous for companies, and how Skillbourg is built to challenge the system, not decorate it.
Everyone keeps saying AI is transforming recruitment. You keep saying it mostly isn’t. What are they missing?
They confuse motion with progress. Recruitment tech got faster at parsing CVs, faster at sending messages, faster at pushing profiles through pipelines, but none of that means it got smarter. If the process is built on weak job descriptions, vague hiring logic, and recruiters drowning in noise, then automation just helps you make bad decisions at scale. That is not innovation. That is industrialised confusion.
A lot of recruitment software did not improve judgment, it just made dysfunction look modern. Nice dashboards, cleaner UX, more automation, same weak outcomes. We do not need more speed layered on top of a broken process. We need systems that can actually understand relevance, context, and fit.
So what exactly are you building that is different?
We are not building another job board with a prettier interface. We are building a recruitment intelligence engine. Most legacy tools behave like digital warehouses: they store profiles, host ads, and force humans to dig through the mess manually. Skillbourg is built to rank, explain, and reduce the chaos before it reaches the recruiter.
We started with a simple idea: recruitment is not a keyword problem, it is a reasoning problem. That is why Skillbourg combines ATS, CRM, and AI‐driven matching in one system, powered by a proprietary recruitment language model trained in the European Union. The point is not to flood teams with more data. It is to show them who matters, why they matter, and what to do next.
You talk a lot about “noise”. What does that actually mean in hiring?
Noise is everything that makes hiring slower while pretending to make it more efficient. It is irrelevant CVs and keyword‐optimised applications that look good but say very little. It is black‐box scores that rank people without explaining why. It is recruiters clicking through endless profiles because the machine did not do the thinking it promised to do.
Now AI has made that worse. We are entering a phase of AI‐generated application spam, synthetic profiles, and low‐intent mass applications. Old systems are collapsing under this new pressure. If your answer is “let’s just automate more”, you are missing the point. The solution is better intelligence, not more digital debris.
So, how does Skillbourg cut through that without becoming another black box?
By refusing the black‐box logic in the first place. If a recruiter cannot understand why a candidate is ranked highly, then the system has already failed. A percentage score on its own is useless. It is not a hiring strategy; it is a guessing game dressed up as math.
That is why we focus on explainable ranking. Skillbourg looks at hard skills, soft skills, role compatibility, expectations, and multiple real‐world factors, then gives recruiters a human‐readable explanation of the match instead of asking them to blindly trust a mysterious number. The recruiter keeps the decision. The AI does the heavy lifting. That is the partnership we believe in.
Why does Europe need its own answer here?
Because hiring in Europe has its own complexity, its own regulatory reality, and its own strategic urgency. We did not want to copy a model built somewhere else and pretend it automatically fits this market. We wanted to solve a European problem with a European mindset: sovereign data, local compliance, trusted AI, and infrastructure that respects the EU AI Act from the start.
The average hiring cycle in Europe is around two months, and that delay is not just administrative pain; it kills momentum, drains cashflow, and slows growth. Recruitment is not a side process anymore. It is operational infrastructure. If Europe wants serious companies, it needs serious hiring systems.
What do you want people to understand about the future of recruitment that they still don’t get today?
The future is not AI replacing recruiters. That is lazy thinking. The future is AI eliminating low‐value work so recruiters can focus on high‐value work: understanding people, challenging assumptions, and making better decisions with less noise.
Another myth needs to die too: more data does not mean better hiring. Better reasoning does. The companies that win will not be the ones with the biggest pile of applications, but the ones with the clearest signal and the courage to build around it. That is the game we are here to change.
