NVIDIA has detailed how enterprises can deploy its NVIDIA AI-Q Research Assistant and Enterprise RAG Blueprints on Amazon Elastic Kubernetes Service (EKS) to build secure, high-performance AI agents capable of accurate, domain-specific reasoning. As organizations seek more reliable generative AI systems, these blueprints combine retrieval-augmented generation (RAG) with NVIDIA Nemotron reasoning models to automate document comprehension, extract insights, and produce high-value analytical reports from large and diverse datasets. The deployment architecture leverages Amazon OpenSearch Serverless as a vector database, Amazon S3 for object storage, and Karpenter for dynamic GPU scaling, ensuring both scalability and cost optimization.
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The RAG blueprint forms the backbone of the system, anchored by NVIDIA NIM microservices-including the Llama-3.3-Nemotron-Super-49B-v1.5 reasoning model and NeMo Retriever Models for multi-modal data extraction across text, tables, and graphics. Built on this foundation, the AI-Q Research Assistant adds deeper agentic workflows, optional use of the Llama-3.3-70B-Instruct model for advanced report generation, and real-time data enrichment through Tavily web search integration. Together, these components enable enterprises to deploy fully featured, GPU-optimized research assistants capable of delivering precise, current, and context-aware intelligence at scale.