Leading the Tech Future: A Conversation With Andrii Matiukha, the Visionary Behind Favbet Tech
In a rapidly evolving tech landscape, Favbet Tech stands out as a company that successfully transformed from its iGaming roots into a full-fledged technology innovator. Its evolution was shaped by the broader strategic thinking of Ukrainian entrepreneur and mentor Andrii Matiukha, founder of the Favbet group. Today, the company specializes in artificial intelligence (AI), cybersecurity, big data personalization, and robust digital infrastructure. In this interview, Andrii Matiukha discusses how teams across the wider technology business ecosystem are building platforms that scale responsibly — and why he views personalization as a form of respect.
Favbet Tech started in the iGaming sector, yet today it’s described as a cutting-edge tech company. How did that evolution happen?
When we began, our focus was on the online gaming industry, but we always had bigger ambitions. Over the years, Favbet Tech evolved from an iGaming operator into a full-fledged technology company with its own R&D center in Ukraine. We expanded our expertise to big data, AI-driven personalization, and digital platform security, areas that go far beyond the entertainment space we started in. This transformation was driven by a desire to leverage the demanding technical environment of iGaming, which requires real-time responsiveness, performance at scale, and highly engaging user experiences, and apply those lessons to build advanced solutions for high-load digital services. Today, our platforms process millions of transactions every day, and we’ve engineered our systems to deliver stability in any environment. In short, we took the DNA of an interactive platform and infused it with a technology-first mindset, focusing on innovation and scalability from day one.
Handling millions of daily transactions is no small feat. How do you ensure the platform remains stable and scalable under such high demand?
Scalability and stability have been the core engineering targets for us. We designed our architecture to be event-driven, meaning every user action is processed in real time by the system. This model lets us instantly adapt content, predict user behavior, and generate recommendations on the fly. In practical terms, it’s like the platform has a reflex – it reacts immediately to each click or interaction, which helps us balance the load and personalize the experience without missing a beat. We also invest in technologies and infrastructure that keep the system stable regardless of spikes in traffic or external stressors. For example, our team has built robust load-balancing and cloud scaling practices, and we’re continually testing failure scenarios so the platform can remain stable in any conditions. The result is an architecture that can elastically handle growth and peaks – scaling responsibly so that new features or surges in usage never compromise the user experience or trust in our service.
AI and personalization seem to be at the heart of Favbet Tech’s strategy. How are you using AI, and what is your philosophy on personalization?
We view AI as an assistant, not a replacement for human decision-making. For example, AI helps determine which features a user might find most relevant and when to present them. But crucially, the AI is there to help our team and our users, not to make autonomous decisions in a black box. My philosophy is that personalization is a form of respect for the user. It means no two users should have the exact same experience – everything from the interface to the timing of notifications is adapted to acknowledge each user’s individual context and preferences. When done right, personalization shows the customer “we understand and value your needs.” Of course, to personalize in a meaningful way, data integrity and quality are paramount – we place great emphasis on clean, well-structured data because better data leads to more effective models and a better experience. In essence, AI helps us deliver a hyper-personalized experience at scale while still requiring human oversight.
You mentioned ethical boundaries. How do you ensure that AI and other innovations are used responsibly, without compromising security or user trust?
This is something I care deeply about. Responsible innovation is about balance. We’re very aggressive in adopting tech that adds value, but we don’t jump on new AI trends without due diligence. For instance, there’s a lot of excitement around generative AI and “hybrid AI” models, but we are cautious about deploying these in sensitive business processes until we’re confident in their safety and reliability. Our approach is engineering-driven: we pilot new technologies internally, validate their outcomes, and scale them quickly only once we know they won’t compromise our platform’s stability or security. A good example is our in-house AI Code Assistant project. Instead of relying on external AI coding tools, which could pose confidentiality risks, we built our own AI assistant that operates within our secure network. This tool helps our developers write and review code faster – it can flag potential bugs or suggest optimizations, but it does so behind our firewall, ensuring complete code privacy and data security. By engineering such solutions ourselves, we maintain transparency and control over how AI is used. Ultimately, trust comes from transparency and consistency – our users and partners know that we embed security and ethics into every layer of our technology. We’d rather take a bit longer with an innovation than rush and risk losing the trust that we’ve built through years of reliable service.