Beeline’s AI Engineer Processes More Than 100,000 Network Fault Reports
In roughly 40% of cases, the system resolved problems independently without requiring human intervention.
Russian telecom operators have spent several years expanding their use of artificial intelligence. Those technologies are also evolving rapidly. Initially, AI was used mainly to improve voice assistants, but today many systems are operating with a high degree of autonomy and making independent decisions. One emerging use case is troubleshooting communications infrastructure.
Network Operations Get a Digital Assistant
Beeline’s networks, for example, have been running an “AI Engineer” for several months. The intelligent agent was designed specifically for incident management. Its official launch took place in March. During the spring alone, the AI Engineer processed more than 100,000 service requests. In approximately 40% of cases, it independently made decisions to resolve failures, eliminating the need for first-line support teams to handle those incidents manually.
“An internally developed system called AI Engineer combines ML models trained on Beeline’s knowledge base, contextual data from verified external sources (RAG), customer instructions, equipment signal information and incident history,” the operator’s press service explained. “The platform connects directly to telecom equipment – including base stations – under strict access-control policies and independently executes commands for diagnostics and fault remediation, including critical operations such as restarting radio modules and boards.”
In practice, the agent was designed specifically to remove as much routine work as possible from company staff. The system achieved that goal even in areas where traditional automation was not physically feasible. As a result, the operator reduced the number of field visits required to respond to failures while also cutting equipment downtime.
Testing Produced Strong Results
The AI Engineer operated in test mode from December of last year through February of this year. The system was initially deployed in several regions, and the trials were considered successful. According to the company, the rollout doubled the speed at which incidents were accepted into processing and reduced average fault-resolution times by 5%. That performance led Beeline to expand deployment of the digital assistant across a broader geographic footprint this spring.
The deployment of an AI agent designed to resolve network failures represents an important milestone for Russia’s telecom infrastructure. The amount of equipment requiring monitoring continues to grow steadily. According to Russia’s Ministry of Digital Development, Russian operators had 948,000 base stations operating across their networks by the end of 2025, an increase of 77,000 over the previous year. AI allows operators to maintain system reliability without continuously expanding maintenance staffing levels.
A New Strategic Partner
Looking ahead, Beeline plans to continue developing AI-related technologies. Some of those projects will be implemented jointly with partners. Just days ago, the operator signed a long-term cooperation agreement with Cloud.ru focused on developing innovative IT technologies and combining efforts on large-scale digital projects. The companies agreed to cooperate in several key areas, including the creation of geographically distributed multicloud architecture, high-speed large-data exchange and the development of low-latency communication channels. The agreement places particular emphasis on the use of AI in the enterprise segment.
Toward Predictive Operations
Other operators are also actively deploying AI to manage infrastructure. Back in 2024, MTS launched a geospatial platform for creating and updating digital 3D terrain maps using generative AI based on neural-network processing of satellite imagery. The system made it possible to select base-station installation sites faster and with greater accuracy.
One of the main future directions for AI development is shifting from simply resolving failures toward predicting and preventing them. That transition could help operators reduce costs even further while improving service quality for users.
