The model, which is developed using AWS cloud infrastructure and NVIDIA AI Enterprise software, will allow traditional telecom networks (L4 and above) to transform into fully autonomous networks.
The multi-modal network operations LLM is designed to manage vast structured data.
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“It is designed to manage vast structured data (events, alarms, counters), unstructured data (logs, MOPs, SOPs, images, text, marketing), and all relevant network data, allowing proactive issue resolution and enhanced service quality,” the company said in a statement.
It is “heavily customised” for telecom networks through training on extensive network datasets and utilising the latest generative AI and agentic AI frameworks, according to the statement.
While telcos have been implementing AI use cases with a transactional approach, achieving true operational efficiency requires a holistic embedding of AI capabilities within the network, it said.
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In the initial development phase, the model will focus on enhancing operational efficiency using “Intelligent Observability.” This will involve two key AI-driven initiatives. First, the Dynamic Network Insights Studio will offer a comprehensive AI-powered network observability solution, providing deep insights into network performance.
The second use case, Proactive Network Anomaly Resolution Hub, will automatically detect and resolve network anomalies without human intervention.
“The shift towards autonomous networks has become imperative within the telecom industry.
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“Through this work, we will empower telcos to reduce operational costs and pave the way for a more agile and resilient network environment,” Manish Mangal, Chief Technology Officer, Telecom & Global Business Head, Network Services, Tech Mahindra, said.
