Artificial intelligence has been a transformative force in technology, helping automate manual tasks, uncover hidden patterns from vast datasets, and manage complex operations.
But for data analyst and AI expert Sergei Shestakov, this is not a surprise. It’s what he’s been working toward for over three decades.
With a PhD in AI and early research focused on autonomous agents and information synthesis, Sergei has been involved in the field since its earliest days, developing a grounded perspective on how to design and integrate AI systems in ways that are both reliable and practical.
Today, as the founder of MPP Labs and MPP Insights, Sergei helps organizations across finance, healthcare, and logistics make better use of their data with AI-powered solutions tailored to real-world challenges. His teams build platforms that combine AI with robust infrastructure to centralize fragmented, unstructured data, giving companies a clearer, real-time view of their operations.
Read on for a closer look at Sergei’s career, work at MPP, and unique approach to AI based on his unparalleled experience.
A Career Spent Preparing for What’s Next
Sergei began exploring AI in the early 1990s during his master’s in computer science at St. Petersburg State Technical University, when the field was still a niche topic even in academic circles. He was interested in its long-term potential and eager to gain hands-on experience, so he pursued a PhD in artificial intelligence.
His research was on the autonomous behavior of agents capable of gathering and synthesizing information from large datasets, with a particular focus on their ability to interpret changing environments, prioritize inputs, and adapt in real-time.
These systems were mostly rules-based and limited to simple keyword matching at the time, unable to handle more complex or ambiguous inputs. But the underlying concepts he explored now underpin technologies like large language models (LLMs), which are behind today’s chatbots and generative tools.
This early foundation gave Sergei a unique perspective on the potential of AI as a practical tool with beneficial applications in complex settings.
His First Steps Embedding AI Into Real-World Systems
Sergei began applying his academic background to real-world engineering while completing his computer science degree. By his fourth year at university, he was managing a team responsible for building enterprise software for a stock depository, giving him valuable insight into how critical systems are developed and maintained.
He went on to lead the development of the electronic payments infrastructure for a national commercial bank, which needed to support high-volume banking operations like ATM networks and point-of-sale transactions.
The infrastructure for the project included early rule-based logic to detect anomalies and automate basic oversight tasks. By embedding these decision-making processes into the platform, he was able to reduce manual intervention and focus on improving reliability and analytical performance.
This was Sergei’s first experience working with applied AI. It gave him a concrete view of how it could be integrated into mission-critical infrastructure while remaining reliable under pressure, an insight that would go on to inform his approach to AI design.
Solving AI Bottlenecks Through Database Design
As AI started growing in complexity and popularity in the late 2010s, Sergei turned his focus to a broader challenge: how to build data infrastructure that could reliably support this increasing scale.
He addressed this in 2018 through a paperpublished by the Association for Computing Machineryin which he proposed a data-centric approach to extending PostgreSQL, a widely used open-source database. Rather than using the database solely as a storage layer, his method integrated business logic and computation directly into the engine, eliminating the need to transfer large volumes of data between the database and external services for processing.
By keeping data and logic in one place, Sergei’s approach helped reduce latency and lower network load. It also enabled more effective parallel processing, where large workloads are broken down into smaller tasks and processed simultaneously, which allows AI systems to respond faster and handle more advanced, resource-intensive queries without adding technical complexity.
The result was an infrastructure better suited to the demands of modern AI, giving models faster, more consistent access to diverse data without sacrificing performance or reliability.
Incorporating AI Into Company Workflows with MPP Insights
In 2022, Sergei foundedMPP Labs, a research and development initiative which provides companies with AI tools tailored to specific operational needs.
This eventually led to the launch ofMPP Insightsin 2025. MPP Insights builds custom AI platforms that help organizations in industries like finance, retail, logistics, and telecom improve how they collect, manage, and analyze data.
It offers features like ETL (extract, transform, load) pipelines, which integrate data from multiple systems into a unified, standardized platform, as well as natural language processing (NLP) tools that extract and structure information from unstructured sources. The company also develops interactive dashboards, reporting tools, and built-in chatbot assistants that help non-technical users explore data, identify patterns, and generate insights.
These solutions can be applied in a range of industries. In healthcare, improved data integration can reveal trends in patient records that support both clinical and administrative decisions while ensuring compliance with standards like FHIR, which governs how health information is structured and shared. In retail, they help teams understand customer behavior more precisely, leading to better offer personalization and inventory management. In logistics, they provide real-time visibility across supply chains, enabling faster and more informed decisions.
These advanced systems are an extension of the work Sergei began decades ago: designing AI that is reliable, scalable, and aligned with how organizations operate.
Building Toward a More Practical AI Future
Sergei Shestakovhas watched AI evolve from a niche academic topic to a central part of modern technology. Along the way, he has continuously adapted his ideas by applying them to projects that expand how AI can be used in real-world operations.
As the broader industry catches up with concepts that Sergei explored early in his career, his attention remains on developing AI that is not only more capable but also more dependable, practical, and aligned with how people and organizations actually work.
That is the future MPP Labs andMPP Insightsare focused on, and it’s one Sergei has been quietly building toward from the very beginning of his storied career.
