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Abstract:

Enterprise AI projects need more than a great model. They need a data foundation that can handle everything a modern AI application depends on: structured records, unstructured content, vector embeddings, and the ingestion and retrieval pipelines that connect them. For most organisations, that foundation is fragmented. Structured data lives in a relational store. Everything else lives somewhere else. And the cost of bridging that gap falls on the developers building the applications.

Db2 unifies both worlds.

By extending its trusted relational foundation with native vector storage, similarity search, and integrations with Unstructured, Db2 becomes the single platform where structured and unstructured data live, are searched, and are served together. Teams can build RAG pipelines and agentic applications that reason across all enterprise knowledge, without adopting separate infrastructure. Early preview releases build further on this with high-performance vector indexing and direct connectivity to external language models, cloud-hosted or on-premises.

The result is a single source of truth for enterprise AI: structured and unstructured data, stored, searched, and governed together, at scale, with the security and reliability Db2 is already trusted to deliver.

If you are building RAG pipelines or agentic applications on enterprise data, this session shows you what Db2 can do for you today, what is coming next, and how to build on a foundation your organisation will not have to replace tomorrow.

Speaker(s): 

Greg Petrossian 

Gregory Petrossian is the Worldwide Go-To-Market Leader for Db2 at IBM and serves as the Db2 LUW Liaison for the International Db2 User Group. With deep expertise across the technology stack, Greg specializes in Agentic AI, Business Intelligence, Data Warehousing, Process Automation, and Enterprise Application Development.


Shaikh Quader

Shaikh Quader has spent over two decades at IBM in software development, with the last ten years focused on AI. Now serving as AI Architect and Master Inventor on IBM Db2, he works on building intelligent, high-performance data platforms — including capabilities such as vector search and LLM integration. He holds many issued patents in AI and data systems and has published peer-reviewed research on machine learning. He is also a Ph.D. candidate at York University, where his research explores the intersection of AI and relational databases.

Christopher Maddock

Christopher Maddock is a technology leader and engineering executive at Unstructured, where he serves as Head of Product and Engineering. In this role, he leads the development of infrastructure that enables enterprises to transform complex, unstructured data like PDFs, documents, and web content into structured, AI-ready formats used by large language models and modern data systems.

Chris has spent much of his career building and leading engineering teams focused on large-scale data and machine learning systems. Prior to joining Unstructured, he held senior leadership roles at companies including Primer AI, AKQA, Accenture, and AXA, where he worked on AI, natural language processing, and enterprise software platforms.

Praveer Gupta

Praveer Gupta is a Product Manager at IBM, where he focuses on advancing the Db2 portfolio and delivering capabilities that improve performance, reliability, and developer experience. With a background in product strategy and agile delivery, he works closely with engineering and cross-functional teams to bring customer-driven innovations to market. Praveer is actively involved in shaping modern data management solutions that support enterprise adoption across hybrid and cloud environments.