I am excited to share that I will be a speaker at the upcoming IBM TechXchange Conference, presenting the following sessions:

2802 - Building Enterprise Agent Solutions with watsonx.data

This session explains how to build enterprise agents using a combination of unstructured and derived structured data from watsonx.data Premium, based on learnings from solutions engineering projects with industry leading IBM customers. We explain how to build solutions that access, ingest and process documents from diverse sources, that index these documents and extract relevant data, and that retrieve relevant data for agents. The pattern is implemented using watsonx.data to process data and via MCP or Retrieval API provide it for agents running on watsonx Orchestrate, with the option to use the Q&A with RAG Solutions Accelerater for advanced question answering with user feedback analytics in that context.

2801 - Enterprise Q&A with RAG and Feedback via watsonx & DataStax

This session demonstrates an advanced enterprise Q&A pattern using RAG, based on real-world IBM customer projects. The solution not only answers user questions using a vector or hybrid index, but also collects and analyzes feedback to identify common topics, measure user satisfaction by topic, and uncover why certain answers were unhelpful. These insights help knowledge owners drive continuous improvement. The pattern is implemented using watsonx.data and watsonx.ai with the Q&A RAG Solutions Accelerator asset, supporting backends such as watsonx.data Milvus, DataStax, or watsonx Discovery for knowledge base and feedback storage.

Also I recommend these Labs:

2753 - Building Insightful Gen AI Workflows with watsonx.data: From Documents to Vectors to Value

Explore how watsonx.data enables Gen AI applications by unifying unstructured and structured data within the Lakehouse. You'll start by processing a document collection into embeddings, indexing it in a vector store, and deriving structured insights from the unstructured content. Using a prebuilt Jupyter Notebook, you’ll query this enriched dataset using the watsonx.data APIs, visualize the results, and learn how to integrate Python-based retrieval workflows into your applications. Whether you're building search, summarization, or RAG pipelines, this lab provides a foundational pattern for Gen AI development on the Lakehouse.

2507 - Harnessing the Power of DataStax HCD Software or Astra DBaaS for Gen AI Solutions

This hands-on lab explains how to use DataStax HCD software on OpenShift (e.g., on AWS) and how to use DataStax via the Astra DB as a service. As a key use case example for Gen AI with watsonx leveraging DataStax HCD, we explain and lead through the lab for how to process documents to populate vectors into DataStax HCD using the Q&A with RAG Accelerator for watsonx, and how to then use the the knowledge represented in DataStax HCD for retrieval augmented answer generation for user questions. We'll also walk through an example of using DataStax vector search for finding related products based on description similarity + product image similarity.

Make sure you register for the: IBM TechXchange Conference October 6-9 in Orlando, FL and get hands-on with the newest tech, meet experts, and code with your peers.#watsonx.data #txc2025session