When people discuss artificial intelligence, most of the attention centers on model breakthroughs. For Gennadii Turutin, however, genuine progress happens when software systems integrate domain knowledge through AI and deliver a meaningful impact on the client experience.
Gennadii’s career spans energy optimization, building automation, fintech, and blockchain. These are fields unified by his drive to turn AI from a concept into a dependable, practical tool.
Gennadii co-founded Archituru, a platform concept born during the Founder Institute accelerator. The project explored how AI could bring order and efficiency to project management in architecture and construction. The project successfully graduated from the Founder Institute accelerator program in January 2025.
Previously, as a Senior Data Engineer at Helix by HL, he contributed to one of the industry’s most advanced AI platforms for private market investments.
Before that, at Prescriptive Data (now Nantum AI), he designed large-scale cloud systems powered by machine-learning applications for intelligent building management, enabling real estate companies and U.S. federal agencies to reduce energy consumption and greenhouse gas emissions.
Most news about artificial intelligence focuses on new large language models or high-profile partnerships between NVIDIA and other industry leaders, while far less attention to how software engineers develop the best practices for integrating AI into our daily lives. To answer these questions, we decided to talk with Gennadii Turutin.
Q: Gennadii, you’re an expert in AI integration with over eight years of experience in tech. Tell us how your journey with AI began.
Gennadii Turutin: My interest in artificial intelligence began long before large language models drew public attention. Nearly a decade ago, I read Rise of the Robots by Martin Ford. The book took a dark view of automation and a “jobless future”, but it raised a question that left a lasting impression on me and made me study statistics and computer science, because I wanted to understand the foundations of AI.
Q: One of the highlights of your career was the GSA* pilot project at Prescriptive Data, which achieved notable cost savings. Could you tell us more about that experience?
Gennadii Turutin: I was part of the team working on the GSA pilot program, where Nantum OS was selected as a finalist in the GSA’s Grid-Interactive Efficient Buildings initiative. The system’s automation and optimization were projected to save about $28.7 million annually across multiple federal buildings. Contributing to that project showed me how AI and machine learning can move beyond theory and make a direct, measurable difference in energy management and sustainability. The company earned multiple distinctions. For example, Nantum OS was named Most Energy Efficient Building Operating System Developer in the 2022 Sustainable Building Awards by BUILD Magazine.
Q: What drove you toward Large Language Models (LLMs) and the next wave of AI?
Gennadii Turutin: It happened gradually. In 2023, I attended the Databricks Data + AI Summit in San Francisco. I remember how one talk stood out. There was a speaker from JPMorgan who explained how they used their proprietary data, to train models that could transform internal workflows. At that point I realized how data can be used by AI to increase efficiency of corporate processes.
When I returned to New York, I immediately began experimenting with LLM applications trained on years of data about client interactions and its impact was impressive.
Q: How did your work in sustainability lead you into the world of fintech and large language models?
Gennadii Turutin: Large language models were the logical extension of the infrastructure work I’d been doing at Nantum AI. I joined Helix by HL to solve very specific problems related to processing documents and using AI at scale. It was very important because the company was developing an AI platform for private market investments where clients can upload their documents and immediately get AI-powered responses along with other functionalities. It is an incredible and innovative idea, and the project later received the New Product Development Award at the 2024 Wealth Management Industry Awards.
At Helix, our goal was to teach AI to analyze complex investment materials, understand the context, find key information, and support decision-making. This meant creating secure and scalable data pipelines, using ad hoc vector databases and vectorization methods, and managing inference services in the cloud.
Q: You’re known in the industry for successfully integrating AI into various fields. What defines a professional integration of AI in finance?
Gennadii Turutin: Finance is a domain that demands trust, precision, and explainability, and applications in this field must be interpretable and auditable. A model should not only produce correct results but also explain its reasoning, protect sensitive information, and comply with established regulations.
Q: You also co-founded Archituru, a company that’s quickly gaining attention for its AI-driven solutions in architecture and construction management. How did that idea originate?
Gennadii Turutin: Archituru began from my interest in using AI to improve decision-making in fields that still depend on experience and instinct. Many architectural projects struggled because their information was scattered across different platforms, and a human brain cannot efficiently process data that comes in different formats, spans multiple files, and requires continuous revisions. When it happens in the context of multiple urgent priorities, the likelihood of a mistake becomes incredibly high, which results in construction delays, financial losses, as well as frayed business relationships.
The idea behind Archituru was to build a platform that uses AI to organize project management, bidding, and cost estimation in one place. It aimed to help firms make design and construction choices based on context-aware AI recommendations. For me, it was a way to apply the same principles I used in finance and energy. I focused on building clear systems, using reliable data, scalable and safe cloud systems, and achieving measurable business outcomes. The high demand for the platform across the industry shows that professionals recognize the need for more advanced, intelligent tools.
Q: We have talked about this synthesis between AI and traditional software, but where do you see the role of AI in software solutions? Will it be considered as an inseparable part of the product or it eventually takes the second role if the hype about AI will fade.
Right now, it sits squarely at the center. There are a lot of experiments with AI, a lot of fantastic products and ideas, and whether it will continue being the driver of software solutions depends on many factors, including whether people will trust the technology and consider it as a positive force, in general.
* GSA — U.S. General Services Administration, a federal agency that manages government buildings, procurement, and infrastructure.
