The Role of AI in Recruiting: Why It Matters
These days, hiring has turned into a huge puzzle with tons of pieces. Companies get hundreds, sometimes thousands of resumes for one job. Old-school ways of reading every line yourself just don’t keep up anymore. That’s where artificial intelligence comes in to help. But the big worry everyone has is simple: will the machine still treat people like people?
Roman Ishchenko saw this problem clearly. He started Raised AI because he noticed something pretty obvious. Out of all the things companies do, recruiting probably creates the biggest pile of messy information, yet most places still handle it the old way—by hand, slowly, and often unfairly. He thought, why not let smart computers do the heavy lifting while humans keep the heart in the process?
Beyond Resumes: Understanding the Full Picture
Most of us know resumes are kind of boring lists. You write down where you worked, what tools you used, and hope someone reads it. But a resume never tells the real story. For example, two engineers can both have “Python” on their resume. One learned it last year in a bootcamp. The other has been building banking systems with it since 2014. Same word, totally different weight.
Raised AI looks past the paper. It pulls together little clues from everywhere. It checks what the old company actually built, which tools were popular there that year, even if that company just got bought or cut half its staff. Imagine a candidate who worked at a startup that suddenly grew from 50 to 500 people. That person probably learned how to handle chaos. The AI notices that. It sees the hidden context humans usually miss because we’re too busy.
A quick real example most recruiters know
Last year a big European bank posted a job for a “Java developer with microservices experience.” Hundreds applied. On paper, 63 people looked perfect. But Raised AI noticed something funny. Twenty of them worked at the same company that had actually moved away from Java two years earlier. Their “current” Java skills were rusty. The system pushed the other 43 to the top. The hiring manager later said they would have invited the wrong group without that heads-up.
The Technical Backbone: How AI Works Behind the Scenes
The platform isn’t one giant brain. It’s more like a team of small experts talking to each other. One part reads resumes super fast. Another understands job descriptions. A third one listens to what recruiters say after interviews—“this person was great on paper but talked too much in the meeting.” Over time it learns the tiny differences that matter for each company.
It scores people on real things: how deep their experience goes with certain tools, how big the projects were, how fresh the knowledge is. Then it keeps getting better every time a recruiter moves a candidate up or down the list. It’s a loop that never really stops learning.
And yes, sometimes it makes small mistakes. That’s okay. Humans fix them, the system remembers, and next month it does better. That’s actually more human than scary-perfect AI.
The Ethical Dimension: Ensuring Fairness in AI Recruiting
Everyone has heard the horror stories: some famous company built an AI that threw away women’s resumes because old data showed mostly men got hired for tech jobs. Raised AI decided early to kill that risk at the root.
Simple rule: the system never sees names, photos, age, gender, schools (unless the job truly needs a specific degree), nothing. It only looks at what you actually did at work. If later they notice, say, people from one country get lower scores even when experience looks the same, the team jumps in, checks why, and fixes the model. They run fairness tests every single month, not just once at launch.
Result? Companies using Raised AI often end up with more diverse shortlists than when humans picked alone. Funny how hiding the personal stuff sometimes brings fairer outcomes.
The Unique Data Foundation: Combining Public and Proprietary Data
Most free tools out there only drink from the public internet well—LinkedIn profiles, GitHub, company websites. That’s useful, but thin. Raised AI adds its own secret sauce: years of real hiring results, thousands of recruiter notes (“great coder but bad team player”), even how fast people replied to messages in the past. All anonymized, of course.
Because of that extra layer, the system can guess who will actually like the company culture, who usually gets an offer, who stays longer than a year. In one case, a fintech startup in London cut their “bad hire” rate from 34% to 11% in six months just because the suggestions felt scarily accurate.
Automating Operational Tasks: Streamlining Recruitment
Recruiters spend way too much time on boring stuff. Writing the same “hey, saw your profile” message fifty times a day. Chasing people for interview slots. Taking notes after calls. Raised AI takes over most of that.
It writes personal-sounding outreach in seconds. Books calendars without twenty emails back and forth. After a call, it spits out a short summary: “Maria loved the tech stack, worried about work-life balance, strong maybe.” Recruiters just read, tweak two words if needed, and get back to talking with humans instead of typing like robots.
The Future of AI in Hiring: More Than Just Efficiency
Look, nobody believes AI will shake hands or spot who’s lying in an interview—yet. But it can already do the boring 80% so people have time for the important 20%. Roman’s dream is that one day the hiring process feels less like gambling and more like finding the right puzzle piece.
Sometimes he jokes that the perfect hire is like dating: the resume is the dating profile photo, but you don’t really know until you talk. Raised AI just makes sure you talk to the right people first instead of swiping past your future star employee because their photo—sorry, resume—was badly written.
Shaping the Future of Recruiting
The world of work keeps changing fast. Remote teams, new tools every six months, skills going out of date quicker than milk. Companies that still hire the slow, old way will fall behind. Raised AI shows one possible path: let machines handle the flood of data, keep humans in charge of the final yes or no, and somehow end up with fairer, smarter teams.
It won’t fix everything. Some jobs will always need gut feeling. Some people hate being judged by algorithms—and that’s fair. But for the millions of open roles right now, a little smart help could mean someone gets their dream job tomorrow instead of waiting six more months.
That’s worth doing.
