{"slug":"at-t-drives-ai-agents-accuracy-efficiency-and-performance-with-nvidia","url":"https://findausecase.com/use-cases/at-t-drives-ai-agents-accuracy-efficiency-and-performance-with-nvidia","title":"AT&T Drives AI Agents' Accuracy, Efficiency, and Performance With NVIDIA","description":"AT&T built 'Ask AT&T' customer service AI agents and worked with implementation partner Quantiphi to use NVIDIA AI Enterprise, NVIDIA NeMo and NIM microservices to build a data-flywheel platform for continuous fine-tuning and evaluation. The pipeline uses NeMo Curator to clean training data, NeMo Customizer to fine-tune base models (Mistral 7B was selected as optimal), NeMo Evaluator to measure performance (Rouge, BERT F1), and NeMo Retriever for up-to-date retrieval, with models deployed as NIM microservices. AT&T reports up to 40% improvement in response accuracy after fine-tuning and an 84% decrease in call center analytics cost, and is collaborating with Arize AI to automate identification of difficult AI interactions.","company":"AT&T","industry":"Telecommunications","aiCapabilities":["Agentic AI","Generative AI","Large Language Models"],"technology":["NVIDIA NeMo","NVIDIA NIM","NVIDIA AI Enterprise","NVIDIA RAPIDS"],"deployment":"Unknown","problemStatement":"Facing challenges like model drift, rising computational demands, and the need for real-time data access, AT&T needed to overcome key challenges: reducing latency, lowering operational costs, and enhancing the accuracy of the models powering its AI agents. With nearly 10,000 documents updated multiple times a week, AI agents need to be updated with the most current information to remain effective, and managing the complexity of multi-agent AI systems was also a pressing concern.","solutionApproach":"AT&T developed 'Ask AT&T' AI agents and worked with implementation partner Quantiphi to leverage NVIDIA AI Enterprise, including NVIDIA NeMo and NIM microservices, to implement a data flywheel approach for continuously enhancing AI agent performance: NeMo Curator cleanses and filters training data; NeMo Customizer fine-tunes base models (Mistral 7B emerged as the optimal performer after testing against Mixtral and Llama); NeMo Evaluator measures performance via metrics like Rouge, BERT F1, question relevance and answer quality; NeMo Retriever gives agents up-to-date access to AT&T's repository so the agents can make decisions and take actions using up-to-date information; and the resulting models are deployed as NIM microservices for optimized inference on GPU-accelerated infrastructure.","businessValue":"AI agent responses showed up to 40% improvement in accuracy with post-training in key metrics, particularly in Rouge and BERT F1 scores, and the deployment of Ask AT&T with NVIDIA NIM and NeMo enabled AT&T to realize an 84% decrease in call center analytics cost. AT&T is also collaborating with Arize AI to automate identification and handling of difficult AI interactions.","evidence":{"band":"high"},"sourceUrl":"https://www.nvidia.com/en-us/case-studies/att-drives-ai-agents-with-nemo/","dates":{"publishedAt":"2026-08-18T09:03:52.366Z","publishedAtSource":"pipeline","updatedAt":"2026-08-26T10:51:01.900Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/at-t-drives-ai-agents-accuracy-efficiency-and-performance-with-nvidia. Bulk republication requires permission."}