Beeline Kazakhstan, a leading digital services operator and a key driver of Kazakhstan’s digital economy, and TelcoBrain Technologies, a global pioneer in the development of techno-economic decision intelligence platforms, announced the successful validation of a joint transformation initiative.
As part of the collaboration, the companies conducted a Proof of Concept (PoC) on an IP network segment in one of Kazakhstan’s cities using the real network topology and live operational data.
During the pilot project, the TelcoBrain platform successfully replicated the structure of the selected network segment and reflected the current state of the network equipment, enabling analysis of infrastructure behavior under conditions as close as possible to the production environment.
The PoC also demonstrated how a digital twin of transport IP/MPLS infrastructure can provide a consolidated 360-degree view of the network by combining physical topology, logical connectivity, traffic parameters, and financial indicators within a single analytical environment. This approach allows technical and business teams to evaluate network development scenarios while taking into account both engineering constraints and economic efficiency.
Among the most notable results of the pilot was the ability to model traffic growth scenarios and forecast network capacity several years ahead, helping identify potential bottlenecks and support long-term infrastructure planning. The platform also demonstrated modernization and replacement scenario modeling capabilities, which can be used in shaping investment plans and procurement programs.
In addition, the pilot showed how techno-economic modeling can provide a more transparent assessment of the total cost of ownership of network segments, including equipment, deployment, energy consumption, site leasing, and operating expenses. This enables a more holistic understanding of CapEx and OpEx dynamics when planning infrastructure development.
A separate area of analysis within the Pilot focused on linking operational network parameters with energy consumption indicators, making it possible to assess the efficiency of infrastructure decisions and their impact on sustainability targets.
The pilot also demonstrated the ability to interact quickly and natively with the platform using artificial intelligence tools, allowing users to obtain analytical insights, visualizations, and scenario assessments through natural language queries. The results of the pilot project confirmed the technical accuracy of the platform’s models and the alignment of its recommendations with existing engineering logic and network operations practices.
Nadezhda Kakusha, Director of Network Technology Development Department
At Beeline Kazakhstan, we evaluate innovation through the lens of its practical value for the business. The pilot project with TelcoBrain allowed us to test a techno-economic approach to infrastructure management and assess its applicability under the real conditions of our network. During the pilot, we analyzed opportunities to improve transparency in decisions affecting network quality, investment efficiency (CapEx), and energy consumption. The results provide an additional basis for further evaluation of such approaches in the context of developing our digital services.
Kamil Raróg, Chief Product & Technology Officer at TelcoBrain
Modern networks generate enormous complexity across engineering, operations, capital planning, and energy efficiency— which can no longer rely on disconnected tools and outdated assumptions. The next generation of operators will rely on intelligence platforms that can model, simulate, and optimize these systems in real time. Through this pilot, we successfully demonstrated how a live digital twin of the network can integrate topology, traffic behavior, operational dependencies, capacity forecasts, and cost drivers into a single analytical and decision environment. We are pleased to work with Beeline on this successful pilot and look forward to supporting the next phase of intelligent network transformation.
