{"slug":"amdocs-builds-generative-ai-agents-for-telecom","url":"https://findausecase.com/use-cases/amdocs-builds-generative-ai-agents-for-telecom","title":"Amdocs Builds Generative AI Agents for Telecom","description":"Amdocs built amAIz, a domain-specific generative AI platform helping telecom companies transform customer experiences, automate processes and optimize decision-making, using NVIDIA DGX Cloud, NVIDIA AI Enterprise software, NVIDIA NIM inference microservices, and NVIDIA Nemotron open-source reasoning models. amAIz agents, enhanced with NVIDIA's Llama Nemotron, autonomously handle complex multistep customer journeys spanning sales, billing and care. Using NVIDIA NIM microservices and telecom-based retrieval-augmented generation, Amdocs reduced tokens consumed by as much as 60 percent for data preprocessing and up to 40 percent for inferencing, and reduced query latency by approximately 80 percent.","company":"Amdocs","industry":"Telecommunications","aiCapabilities":["Generative AI","Retrieval-Augmented Generation","Agentic AI"],"technology":["NVIDIA DGX Cloud","NVIDIA AI Enterprise","NVIDIA NIM","NVIDIA Nemotron","NVIDIA Llama Nemotron"],"deployment":"Public Cloud","problemStatement":"Telecom companies need to transform customer experiences, automate processes, and optimize decision-making, while managing the cost and latency of deploying generative AI at scale for complex, multistep customer journeys spanning sales, billing and care.","solutionApproach":"Amdocs built amAIz, a domain-specific generative AI platform, using NVIDIA DGX Cloud and NVIDIA AI Enterprise software to develop and deliver solutions based on commercially available and domain-adapted LLMs, NVIDIA NIM inference microservices to accelerate deployment, and NVIDIA Nemotron open-source reasoning models. amAIz agents, enhanced with NVIDIA's Llama Nemotron, autonomously handle complex multistep customer journeys, using telecom-based retrieval-augmented generation on NVIDIA infrastructure.","businessValue":"Using NVIDIA NIM microservices, Amdocs reduced the number of tokens consumed for deployed use cases by as much as 60 percent for data preprocessing and up to 40 percent for inferencing, achieving the same accuracy at lower cost per token. The collaboration also reduced query latency by approximately 80 percent, delivering near-real-time responses.","evidence":{"band":"high"},"sourceUrl":"https://www.nvidia.com/en-us/case-studies/amdocs-builds-generative-ai-agents-for-telecom","dates":{"publishedAt":"2026-08-15T23:34:52.843Z","publishedAtSource":"ledger","updatedAt":"2026-08-18T13:38:21.052Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/amdocs-builds-generative-ai-agents-for-telecom. Bulk republication requires permission."}