Build a Document Intelligence Pipeline With Nemotron RAG | Nemotron Labs

​What if you could ask questions about complex PDFs—dense tables, charts, and scanned pages—and have an AI agent answer with precise, cited snippets from the original document, without hand-writing brittle parsers? With Nemotron RAG, you can build a multimodal intelligent document processing  pipeline from scratch in under an hour, using GPU-accelerated extraction and just a few hundred lines of Python to turn unstructured files into grounded, queryable knowledge.

​Curious how it works? Join us for a developer-focused livestream that takes you from first principles to running code, showing exactly how Nemotron RAG powers end-to-end document processing for real-world reports. We’ll cover, step by step:

• ​A live demo: building and running an intelligent document processing agent built with Nemotron RAGthat “reads” PDFs, preserves tables, and understands charts in real time.

• ​How to plug NeMo Retriever library and Nemotron RAG models to structure text, tables (as markdown), and chart crops for high‑recall multimodal retrieval.

• ​How to tune chunking, embeddings, and reranking so Nemotron RAG can reliably surface the few pages or tables that actually answer complex queries.

• ​Wiring the top results into a Nemotron‑powered assistant that returns grounded, traceable answers with citations to the exact page, table, or figure.

​By the end, you’ll understand how Nemotron RAG retrieves information from documents—and you’ll leave with a reusable Python pipeline you can adapt for your own intelligent document processing workloads.