The AI researcher and founder of a next-generation recruiting platform explains why the hiring market is “broken,” how artificial intelligence has changed the way companies search for talent, and what needs to be done to bring the human factor back into recruiting.
Hiring today is one of the fastest-changing areas of business. Companies struggle to find qualified specialists, while candidates use AI tools to generate resumes and cover letters, flooding recruiters with hundreds of nearly identical applications. To handle this volume, HR departments turn to AI filters — but those algorithms often reproduce bias and can’t adapt to a company’s real needs. As a result, inbound recruiting is declining: it still works for mass or junior roles, but rarely for qualified positions.
Roman Ishchenko, PhD in Mathematics with applications in Computer Science, founder and CEO of Raised AI, and author of research papers on AI-driven systems, has spent the last several years building technologies that make hiring faster, more precise, and centered on real people.
In this article, based on that interview with Roman Ishchenko, we explore how the platform was created, why it emerged, and how his team is shaping the future of recruiting by building and training a human-centered AI system designed to make technology serve people.
Why is the hiring market broken today?
What used to be a human-to-human process is quickly turning into a dialogue between algorithms. Candidates use AI to write and rehearse their answers, while recruiters depend on AI systems to evaluate them. On the other side, recruiters and companies are also deploying AI agents to screen resumes, conduct interviews, and make hiring recommendations.
*“This shift, in my opinion, will eventually make inbound hiring obsolete — it’s already on its last breath,”* says Roman Ishchenko.
According to the expert, the symmetrical use of AI on both sides makes the process less transparent. When algorithms select candidates and candidates respond with algorithmic help, the essence of human communication is lost. Interviews not turn into a dialogue between people, but into an interaction between two systems trained to recognize and adapt to patterns.
*One major problem,” he adds, “is that recruiters let AI manage them instead of managing AI. Many recruiters let AI think for them — for example, asking ChatGPT what interview questions to use or what evaluation criteria to apply. It should be the opposite: recruiters set the direction, AI executes and supports.”*
How to make hiring more human-centered with AI?
Most candidates today go through AI-powered interviews but still prefer to meet a real person. When a candidate has two or three offers, they often choose the company that invested more effort into building a relationship with them. It’s also the recruiter who knows what offers the candidate has and what concerns they might have, because they built that relationship. For now, humans still prefer relationships with humans, not AI.
That’s why Roman Ishchenko decided to take a different approach to using AI. The idea behind his company is not to replace recruiters but to empower them — to make them more productive and focused on meaningful interaction rather than handling repetitive tasks.*“We believe that around 50% of the work can be automated,” Roman Ishchenko notes. “Our engaged pool of candidates allows us to close positions faster than traditional agencies and with people who wouldn’t usually respond to a LinkedIn message.”*
Around half of all placements come from the internal database, which continues to grow. The company’s AI handles sourcing across internal and external platforms and manages initial candidate communication.
The digital assistant Scout acts as a recruiter’s co-pilot: finding and mapping candidates, holding first chats, and organizing the pipeline with meeting summaries, follow-ups, and submission forms. Recruiters then step in for interviews and final selection, ensuring every candidate is evaluated with human judgment and empathy.
How was it trained?
Raised AI continuously improves its system through feedback from its in-house team of senior recruiters and from client companies using the platform. According to Roman Ishchenko, the key factor in achieving quality results is proprietary data.
*“Most tools rely on LinkedIn, but that’s a huge limitation,” he explains. “Many candidates don’t have complete or updated profiles — an engineer might just write ‘Software Engineer’ without mentioning their tech stack or current project. On top of that, LinkedIn actively restricts access to its data. So having our own dataset is critical — both short-term and long-term.”*
To solve this problem and continuously improve the algorithms, Roman’s team collects unique candidate data and uses it to train specialized models. Each job description is decomposed into separate criteria: skills, domain, industry, location, language, leadership level, and individual models are trained for each.
The system functions as a retrieval-augmented generation (RAG) pipeline: For example, to evaluate a candidate’s Python expertise, a dedicated model retrieves information from the internal knowledge base on how such skills are assessed and generates a corresponding score. Different large language models are used for different parts of the process.
The future of recruiting
Today, candidates in Raised AI's database reply in about 90% of cases, compared to roughly 20% in typical LinkedIn cold outreach. This allows the company to deliver the first candidates to clients as soon as the next day.
Automation has significantly increased recruiters’ productivity. Since about half of a recruiter’s time is typically spent on sourcing and chatting, automating these stages enables each specialist to handle twice as many candidates without losing quality.
At the same time, precision has improved. With AI-assisted evaluation, recruiters make fewer mistakes, and every candidate submitted is already a strong fit. From a business perspective, automating and standardizing much of the process makes the model scalable, with margins close to SaaS companies. That’s one of the reasons the approach has drawn strong investor interest.
Roman Ishchenko’s vision has been validated by several major accelerators, including 500 Global и UltraVC, which supported Raised AI’s development and helped the company secure funding. Many entrepreneurs in these programs face the same recruiting challenges and clearly see the need for new AI-driven solutions.
*“Accelerators bring many benefits — investments, networking, mentorship, and international exposure,” says Roman Ishchenko. “But for me, the most valuable part is the community. I joined programs not just for funding but to stay close to other founders, mentors, and industry experts to share recruiting trends and build stronger expert networks. That’s how we grow as an ecosystem, and I believe this is the right path forward.*”
