We build for speed across every layer of the stack — runtime efficiency, accelerated pipelines, lean automation, and decisions without lag. Everything we build and document — from inference optimization to quantum encryption — serves one goal: reducing the distance between data and decision.
Lecturer and researcher in quantum and applied machine learning at ZHAW, and academic lead of the Innosuisse AI Booster Expert Group on Quantum Algorithms. Pavel has 9+ years in applied data science across bioinformatics, banking, network analysis, and online services. He is a PhD in Theoretical Computer Science from HSE University (Moscow). His publication record includes peer-reviewed work in computational biology journals and conference proceedings. Before moving into research and teaching, he led data science teams at BetVictor and Sberbank.
Software engineer with 10+ years of experience building data-intensive applications in Java, Kotlin, and Python. Sergey currently designs backend systems at Plata Card (fintech). He previously built microservices at Arrival, the British EV startup, and spent four years at HeadHunter (hh.ru), Russia's largest job platform, where he led the ads service that became the company's second-largest revenue source. He holds a Master's in Mechatronics from Bauman Moscow State Technical University and has published research on control systems.
Speed is a design constraint, not a feature. The best systems aren't the most sophisticated on paper — they're the ones that reach production without delays, stay healthy without constant intervention, and can be improved without a rewrite. We build toward that bar across four fronts: runtime performance, model-to-market pipelines, agent orchestration, and quantum computing for cases where classical throughput hits its ceiling.
We ship products and publish the full story: what we tried, where we hit the wall, and how we measured the result. Each write-up follows the same five sections — Problem, Challenge, Solution, Implementation, Summary — so the performance tradeoffs are visible, not buried in a slide deck.
Pavel handles data science, ML, and quantum computing. Sergey handles backend systems and infrastructure. Between the two of us, we cover the full path: from a slow prototype to a performant, monitored, explainable production system.
Free assessment or booked call. We look at what you have, find the speed bottlenecks, and come back with a concrete proposal.
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