Digital Blueprint: Rosreestr Tests AI Based on Russian Government Office Experience
The initiative is designed to reduce errors and speed up government services. Over time, projects like this are expected to establish a standard for using artificial intelligence across government agencies.
Rosreestr (Federal Service for State Registration, Cadastre and Cartography) is testing a standardized artificial intelligence solution developed using the experience of the Office of the Government of the Russian Federation. The technology is intended to automate the agency's internal operations, process information more efficiently and improve the quality of public services. It represents another step toward the adoption of standardized AI solutions that can later be adapted and replicated across government agencies. That approach is creating sustained demand for Russian machine learning platforms, document processing systems, intelligent search technologies and decision support tools. AI is expected to reduce document review times while minimizing technical errors.
Integration With the National Digital Platform or Export Potential?
Following testing, the AI solution could be used by Rosreestr to perform preliminary reviews of applications and supporting documents, classify incoming requests, detect errors and inconsistencies, prepare draft decisions, analyze data from the Unified State Register of Real Estate (EGRN), and identify previously unregistered real estate properties. Rosreestr already has the technological foundation for these functions. The agency operates EVA (Digital Registrar Assistant), a digital assistant that uses neural networks to recognize documents and perform preliminary automated verification.
One of the project's most promising directions is integrating AI with the Natsionalnaya sistema prostranstvennykh dannykh (National Spatial Data System), the unified digital platform that consolidates information on land and real estate. The platform is intended to serve as a common information foundation for citizens, businesses and government agencies. Some of the project's components – including systems for recognizing property title documents, intelligent data validation, electronic registry analysis and spatial information management – could also find applications in CIS countries and other nations modernizing their land administration and cadastral systems.
How AI Became an Essential Assistant
Rosreestr's first AI service – the EVA (Digital Registrar Assistant) mentioned above – was launched in 2022. Using pre-trained neural networks, EVA recognizes incoming documents, converts them into machine-readable format and helps conduct preliminary reviews. By the end of 2023, EVA had been deployed in 36 Russian regions, processing up to 4,500 document packages every day.
In 2024, Russia launched the Natsionalnaya sistema prostranstvennykh dannykh (National Spatial Data System), a unified digital platform bringing together government land and real estate data. The following year saw the establishment of the Artificial Intelligence Development Center, one of whose primary objectives is identifying and replicating standardized AI solutions across federal agencies and regional governments. Rosreestr also began introducing AI technologies to identify previously unregistered real estate properties and accelerate cadastral valuation during service delivery. In 2025 and 2026, AI was already being used by the Office of the Government of the Russian Federation to monitor national projects and government programs. The model analyzes activities and performance indicators to identify potential risks. That experience became the basis for preparing standardized AI implementation projects for other government agencies.
A New Standard for Government Agencies
Initially, AI will most likely perform supporting functions within Rosreestr by sorting and reviewing documents, identifying inconsistencies, processing incoming requests and preparing recommendations for employees. Legally binding decisions on property registration, however, will remain the responsibility of government registrars. If the pilot proves successful, the solution could be deployed across Rosreestr's regional offices and later adapted for other federal agencies. Its broader adoption will depend on the quality of government data, information security, personal data protection and the ability to explain how AI-generated results are produced.
Over the longer term, systems like these could shorten government service delivery times while establishing a unified technological standard for AI adoption across public agencies.
