Written by Spencer Hulse
The founder of Raised AI, a company that helps businesses improve the efficiency and speed of hiring through artificial intelligence, Roman Ishchenko, shared how technology enables companies to reduce costs and find the right candidates even in highly specialized fields such as Web3, blockchain, and the crypto industry.
October 23 — In recent years, the hiring market has entered a new phase of transformation. The explosion of AI tools, the rise of remote and decentralized teams, and the emergence of entire industries like Web3, blockchain, and crypto have made traditional recruiting models increasingly inefficient. Companies face not only the challenge of finding qualified specialists, but also the growing costs and risks associated with making incorrect hires, which can reach up to five times a candidate’s annual salary.
At the same time, AI has become both a problem and a solution: while it creates a flood of synthetic resumes and deepfake interviews, it also offers a way to rebuild recruiting from the ground up. The question is how to use it wisely — to make hiring faster, cheaper, and smarter without losing the human touch.
Roman Ishchenko, PhD in Mathematics and founder of Raised AI, set out to answer that question. After years of launching AI and data-driven products inside major corporations such as Mastercard and consulting for Fortune 500 clients, he decided to reimagine recruiting as a product. His company, backed by 500 Global and Ultra VC, automates nearly half of the hiring cycle from sourcing to screening, reducing costs by up to 50% and cutting time-to-hire by 60%.
In this interview, Roman explains how AI can transform hiring economics, what makes recruitment in crypto and blockchain fundamentally different, and why the future of work depends on striking a balance between algorithmic precision and human expertise.
Roman, you founded a company that uses artificial intelligence to make hiring not only faster but fundamentally more efficient. What parts of the recruiting process have you automated, and how does this translate into measurable financial impact for your clients?
At Raised, we’ve automated nearly half of the recruitment cycle — from sourcing and outreach to candidate screening and reporting. This allows us to deliver hires up to 2× more cost-efficiently than traditional agencies or in-house teams. Several of our clients now outsource 10–20+ hires per year to us under long-term contracts, effectively replacing their internal recruiting departments. By combining AI, data, and recruiter expertise, we achieve a level of efficiency that’s hard to match internally. As AI continues to reduce the marginal cost of each hire, outsourcing recruitment becomes increasingly rational. Clients also see up to 60% faster time-to-hire, which directly drives revenue. Every month saved in filling a key role can mean hundreds of thousands in additional income. Our probation success rate is close to 100%, thanks to data-driven matching and our proprietary candidate base, resulting in fewer failed hires, less onboarding waste, and stronger ROI.
When a company makes a hiring mistake, what kind of financial and operational losses are we actually talking about?
It varies widely by role, but in our case, we focus on high-level specialists — senior ML engineers, AI product leaders, and commercial executives such as Heads of Sales or VPs of Marketing. For these positions, the cost of a wrong hire goes far beyond the recruiting fee. For example, a mis-hire in a Head of Sales position can mean missing quarterly revenue targets, delaying the next fundraising round, or even jeopardizing the company’s survival. When you factor in lost productivity, team disruption, onboarding expenses, and the opportunity cost of delayed execution, the total loss can easily reach three to five times the person’s annual salary. That’s why we place such a strong emphasis on precision matching. Our AI doesn’t just assess technical skills — it also analyzes cultural and contextual fit, predicting how a candidate will perform in a specific company environment. This holistic approach dramatically reduces the risk of a poor match and is one of the main reasons our clients continue to work with us across multiple roles.
Yes, your platform achieves an impressive probation success rate close to 100%. How do you build a process that ensures such strong matches between candidates and companies?
We combine AI precision with human expertise. Our matching algorithms analyze thousands of data points from candidate skills and career history to market benchmarks to identify the best-fit talent in a fraction of the usual time. That’s what drives both our speed and scalability. At the same time, the human layer plays a crucial role. Every client works with recruiting partners who are experts in their respective domains, for example, a former engineer leading searches for machine learning roles or a former sales leading recruiting for go-to-market talent. Thanks to that experience, our team doesn’t just execute job descriptions; they help clients define them. Many of our strongest results come from this collaboration. Using insights from market data, past placements, and performance feedback, we often guide clients in refining the scope of a role, sometimes suggesting a slightly different profile that’s proven to perform better in similar cases.
Do you rely on your own proprietary database, or do you use a hybrid approach that combines AI with open-data analytics?
Nearly half of our placements now come directly from our proprietary talent base, which grows with every new project. Each role we work on generates structured data about candidates — their skills, industries, behavioral patterns, and hiring outcomes — continuously improving our matching models. Over time, this feedback loop becomes a strong competitive moat. We still use external data sources when needed, but our long-term goal is to become a fully self-sufficient marketplace where most hiring happens within our own ecosystem. As our database expands, we’re able to deliver results that are far more accurate and several times faster than what in-house teams or traditional agencies can achieve. Unlike platforms such as LinkedIn, which often provide only surface-level information, titles, companies, and locations, our system captures deeper insights: what technologies people actually use, the kinds of projects they’ve worked on, and the types of company environments where they perform best. Having already built long-term, trusted relationships with candidates — supported by our AI career-coach assistant — we can go directly to the few professionals who are the best fit and ready to engage, rather than contacting hundreds to fill a single role. We can immediately connect with the few who are the best fit and ready to engage right now. Each search enriches our data and improves the next one, creating a compounding efficiency that scales naturally. Ultimately, our vision is to build the world’s most intelligent recruiting network, a living, self-learning ecosystem where companies can hire top professionals seamlessly, and candidates rely on «Raised» as their trusted AI-powered career partner.
You’ve worked extensively with Web3 and blockchain companies and have been personally passionate about this space since your time at Mastercard, where you worked on digital innovation projects with major fintech players. How do you approach finding specialists for such a field — do you also work with industry experts, or is the approach different here?
Our AI models are specifically trained for Web3 recruiting, allowing us to evaluate candidates using criteria unique to the industry, such as on-chain experience, DAO participation, understanding of tokenomics, and work within decentralized ecosystems. This specialization enables us to identify top-tier talent that generic recruiting tools would typically overlook. At the same time, we’ve built a dedicated network of Web3 professionals and collaborate with recruiters who have real, hands-on experience in this field. Many of them have worked directly with Web3 startups, protocols, or venture funds. This blend of domain fluency and data-driven intelligence allows us to assess not only technical expertise but also cultural and contextual fit with high accuracy. Today, I see the crypto industry going through a real renaissance in the U.S., with strong momentum returning around infrastructure, fintech, and blockchain innovation. That’s why combining AI precision with insider expertise has become so crucial — it’s what allows us to consistently close even the most complex and specialized roles faster and more effectively than traditional firms.
What are the unique characteristics of hiring in the crypto and blockchain sector? How does it differ from traditional IT recruitment, and what specific challenges do companies face there?
The first major difference lies in where and how talent exists online. Many top crypto professionals don’t build their personal brands on LinkedIn — instead, they’re active on X, Discord, or DAO communities, often under pseudonyms. They communicate in a completely different «language», shaped by Web3 culture, governance models, and community-driven projects. As a result, recruiters have to adapt their entire approach from data sources and outreach channels to how they assess credibility and engagement. Another challenge is market depth. The Web3 talent pool is relatively small, and the best specialists are often already engaged in passion-driven projects or early-stage ventures. This makes relationship-based recruiting and proprietary databases far more critical than in traditional B2B software roles, where the market is broader and more transparent. Finally, the industry evolves at a remarkable pace — new technologies, protocols, and narratives emerge every few months, which means recruiters must have genuine domain fluency to distinguish real expertise from hype. It’s no longer enough to simply understand technical skills; one must also grasp ecosystems, token models, and the reputation dynamics within communities.
Roman, based on your experience, can AI-driven hiring optimization be seen as more than just an HR tool, as a factor that directly affects business profitability?
The best founders and companies don’t view hiring as a cost to minimize; they see it as a strategic investment that defines the trajectory of their business. This is especially clear in fields like AI and deep tech, where a single key hire, such as a world-class machine learning engineer or product leader, can determine whether a company captures or loses its market. Top engineers today command multi-million-dollar packages not because companies want to overspend but because the return on exceptional talent is immense. AI-driven automation takes this further by making the entire process faster, smarter, and more cost-effective. From a financial standpoint, the ROI is measured across several dimensions: reduced cost per hire through automation of routine tasks, faster execution that translates directly into revenue gains, and higher retention resulting from stronger matches and better cultural fit. For example, every month saved in hiring a key go-to-market role can mean hundreds of thousands in additional revenue. So yes, AI-driven hiring is far more than an HR efficiency tool; it directly impacts profitability. Its real strength lies in helping companies scale intelligently and turning talent acquisition from a reactive necessity into a core competitive advantage.
This industry announcement article is for informational and educational purposes only and does not constitute financial or investment advice.
