Panos Vagenas is an Advisory Engineer at IBM Research, leading development efforts in the intersection of Artificial Intelligence, Information Retrieval, and Data Management. At IBM since 2020, Panos has made key contributions to various innovative AI technologies, such as Docling. With an engineering career spanning from 2014, he has significant experience in designing and developing enterprise software solutions for industry-leading organizations. Panos holds an MSc in Computer Science from ETH Zurich and has received various awards including the IBM Outstanding Technical Achievement Award, the IBM Technical Collaboration Achievement Program Leadership Award, the ACM SIGMOD Best Demonstration Award, and the ETH Zurich Excellence Scholarship.
Panos Vagenas is an Advisory Engineer at IBM Research, leading development efforts in the intersection of Artificial Intelligence, Information Retrieval, and Data Management. At IBM since 2020, Panos has made key contributions to various innovative AI technologies, such as Docling. With an engineering career spanning from 2014, he has significant experience in designing and developing enterprise software solutions for industry-leading organizations. Panos holds an MSc in Computer Science from ETH Zurich and has received various awards including the IBM Outstanding Technical Achievement Award, the IBM Technical Collaboration Achievement Program Leadership Award, the ACM SIGMOD Best Demonstration Award, and the ETH Zurich Excellence Scholarship.
Docling is rapidly becoming the de-facto standard in open-source document AI. The project has achieved remarkable adoption with over 30,000 GitHub stars, more than 500,000 monthly downloads, and trending to #1 repository globally on GitHub. Now incubated as a Linux Foundation AI & Data project, Docling provides enterprise-grade capabilities for parsing complex layouts, extracting tables, and converting unstructured documents into AI-ready structured formats.
In this workshop, we will use Docling to build workflows ranging from document extraction and conversion into different formats, to advanced RAG and agentic AI applications — covering a wide variety of document AI use cases.
This session is meant for AI tech leaders and builders who would like to learn how to make the most out of their unstructured data using Docling.
We first introduce Docling, the emerging de facto standard for open-source document processing for GenAI and beyond, with over 40K GitHub stars, 1.5M downloads/month, and repeated #1 rankings on GitHub and HuggingFace trending boards. We illustrate the challenges it addresses and outline its architecture, including our AI models that power its vision capabilities, as well as the unified data model.
After the introduction, we move to the workshop part, where we walk through various Docling capabilities, starting from basic usage, covering document conversion and exploration, moving on to rich native chunking capabilities, over to end-to-end scenarios such as advanced RAG pipelines including multimodal support and visual grounding, and last but not least agentic workflows leveraging Docling MCP.
By the end of the workshop, participants will have a solid understanding of how Docling significantly simplifies, accelerates, and standardizes working with unstructured data, and they will be able to apply to this knowledge to various scenarios be it typical GenAI use cases like RAG or ultimately any document-related workflow.
Tech leaders, engineering managers, software developers, and practitioners who would like to make the most out of their documents — for GenAI (e.g. RAG) & beyond
Git, Python, Pip installed. Experience with Python, basic understanding or GenAI workflows such as RAG.
Workshop materials will be available here: https://github.com/docling-project/docling-workshops
A discussion of building global success and how artificial intelligence is transforming the world.
