Industry sources report that Sberbank and Gazprom Space Systems have reached an agreement to collaborate on artificial intelligence initiatives. The collaboration signals a strategic push to combine the financial technology strengths of Sber with the space data and analytics capabilities of Gazprom Space Systems, aiming to accelerate AI-driven developments within Russia and its energy sector.
The accord was inked by Andrey Dmitriev, General Manager, along with the Head of the Customer Transformation Directorate at Sberbank CIB, and Dmitry Sevastyanov, General Manager of Gazprom Space Systems. Their joint leadership underscores a mutual commitment to advance shared AI objectives and to explore cross-sector applications in high-value markets.
The memorandum outlines plans for building geoanalytical services tailored to the oil and gas industry. Its purpose is to advance machine learning and artificial intelligence technologies that leverage Russian expertise and data. A key element involves integrating Gazprom Space Systems JSC’s experience in aerospace monitoring with the Sberbank Geometry geoanalytic platform to create enhanced solutions for monitoring oil and gas assets and environments.
One notable aspect of the collaboration is the development of an innovative aviation monitoring system named SMOTR. This system is envisioned to utilize ultra-high-resolution optoelectronic satellites alongside radar satellites to enable all-weather, around-the-clock monitoring of critical facilities and regions. Sberbank experts are expected to contribute to the software development and testing phases, ensuring robust analytics and reliable operational performance.
For context, the Geometry geoplatform offers capabilities to process imagery from satellites, drones, and video sources. It enables the classification of objects such as fields, forests, and constructions and supports analysis of their attributes across multiple parameters. This foundation is intended to streamline AI product creation by providing structured, pre-labeled data that accelerates development cycles and reduces the time to market.
In practical terms, pre-labeled data accelerates the deployment of AI solutions by shortening the training phase for models. The collaboration emphasizes leveraging this data-driven approach to deliver faster, more accurate insights in oil and gas operations, potentially enhancing monitoring reliability, risk assessment, and asset optimization across the sector.