How do you ground generative AI in trusted enterprise policies and regulatory guidance? Discover how retrieval-augmented generation (RAG) on Red Hat AI Factory with NVIDIA delivers context-aware, accurate LLM responses.
Enterprise data rarely sits in one place. In regulated sectors like financial services, anti-money-laundering (AML) investigators must evaluate public regulations alongside internal company procedures and customer records. Standard off-the-shelf LLMs understand general concepts, but they lack visibility into your organization’s specific policies. In this video, Taylor Smith demonstrates an enterprise AI quickstart based on NVIDIA’s Enterprise RAG Blueprint and adapted for Red Hat AI Factory with NVIDIA. See how RAG retrieves trusted document passages, reranks search results, and grounds model outputs—while keeping human decision-makers in full control. You’ll learn how RAG retrieves and grounds internal enterprise data before generating responses and how to extend foundational RAG into autonomous, agentic workflows.
00:00 Introduction to Enterprise RAG & AML investigations
00:40 How RAG grounds enterprise LLM responses
01:12 Enterprise RAG AI quickstart overview
02:00 Document ingestion, embeddings & Milvus vector search
02:56 AML investigation demo
05:05 OpenShift Infrastructure model serving and monitoring
05:52 Platform observability with Prometheus & Grafana
07:15 Summary: Extending RAG into agentic workflows
Explore how Red Hat AI Factory speeds the path to production AI:
✨ Try your own quickstart → https://red.ht/aml-rag-nvidia-quickstart
🤖 Discover Red Hat AI Factory with NVIDIA → https://red.ht/AIFactory
📄 Read the Red Hat AI Factory datasheet → https://red.ht/ai-factory-nvidia-datasheet
#RAG #vLLM #OpenShift #EnterpriseAI #RedHatAI #NVIDIA #RedHat
