Event Schedule

Modern user queries require a mix of structured and unstructured data in order to achieve satisfactory retrieval performance. This is where traditional search methods fall short. In this talk, we dive into retrieval evaluation, comparing keyword, vector, hybrid, and late-interaction models with Superlinked's mixture-of-encoders approach. We examine how each approach fares in real-world scenarios (e.g. a query for "5 guests under $200 with 4.8+ rating"). Using benchmark datasets and real production use cases, we share metrics, evaluation methodology, and common pitfalls. We introduce Superlinked's mixture-of-encoders approach, where dedicated encoders for various data types like text, numbers, and categories combined with LLM-driven query understanding enable more accurate and scalable retrieval. Finally, we discuss how to productionize this system and share use cases from travel to e-commerce, pointing toward the future of multi-attribute and meta data aware embeddings search.

At Bayer, we started building an internal GenAI platform called myGenAssist a little over 2 years ago. What started out as a small prototype, has now grown into a global platform used by tens of thousands of employees every month across Bayer and its subsidiaries. In this presentation, I will explain the advanced features of the platform (research agents connected to many internal and external data sources, a low-code workflow automation engine & more), how we were able to scale it and the challenges we faced along the way. I will offer insights into the technical architecture, design decisions and considerations as well as the non-technical factors influencing the success or failure of a product like this.

Vector databases became the de facto solution for embedding-based retrieval, which reveals its limits as users realize that similarity is not relevance. As a workaround, current solutions offer "hybrid retrieval" implemented as separate queries on disjoint indexes with late fusion of partial results based on rank or scores. In this talk, we present a fundamentally different model for billion‑scale hybrid retrieval, built from the ground up to address these challenges. Our storage format and query engine were designed to unify dense & sparse vectors, keywords, filters, and user‑defined scoring functions into a single distributed query, without relying on separate indexes and late fusion. This approach gives us a flexible query language that enables search practitioners to optimize relevance in their respective domains without having to manage and sync multiple data stores.

Search in enterprise settings is uniquely challenging—documents are siloed, context is implicit, and user queries are often ambiguous. At Box, we built the DeepSearch Agent, a multi-step, agentic retrieval system powered by LangGraph, to address these complexities. Unlike traditional search systems, DeepSearch orchestrates embedding and keyword based retrieval, LLM-based re-ranking, semantic filtering, and metadata extraction in a dynamic, modular flow. Each component was iteratively improved and deployed in production, enabling rapid experimentation and measurable improvements in answer recall. This talk walks through our journey evolving DeepSearch from static rerankers to a fully agentic system, including how we built evaluation loops, debug tools, and fallback strategies to make it reliable at scale. Attendees will leave with architecture patterns and practical lessons for building composable, AI-powered retrieval systems tailored to real-world enterprise usecases.

Getting good results from a search engine is hard. Too hard. We all know that the virtuous circle of search algorithms, then search measurement and analysis, then search tuning, and back to search algorithms. New algorithms are available from search vendor; and there are more and more tools for measurement and analysis (OpenSearch UBI, OpenSource Connections Quepid, OpenSearch Search Relevance Workbench). But experimentation is slow and tuning is still manual. Now, though, by taking advantage of LLM-based agents combined with interleaved A/B testing, we can automate the process, making it faster and more accurate. Building on an agentic infrastructure, we create a collection of agents, each specialized in a particular business problem and incorporating a variety of search strategies. The agents not only create tests and evaluate them, but also orchestrate their deployment.

Any successful Retrieval Augmented Generation (RAG) system hinges on its ability to retrieve relevant context. This talk presents key lessons learned from our participation in this year's SIGIR LiveRAG challenge, where we were tasked with building a RAG system to generate accurate answers under time constraints. We will deconstruct the RAG pipeline we developed, focusing on our journey to improve the retrieval component. Specifically, we'll highlight how synthetic data generation was crucial in the evaluation process. Beyond the theoretical, we'll also share practical insights into the engineering challenges of running a large-scale RAG system locally and the solutions we implemented to overcome them. This talk is for anyone interested in building more robust and reliable RAG applications.

Misinformation travels faster than ever, but AI can help us fight back. In this session, we share how we built a platform combining search, an intelligent assistant, and a Retrieval-Augmented Generation (RAG) system to support fact-checking and news classification. By empowering journalists and editors with actionable insights, we explore how AI can promote transparency, strengthen critical thinking, and rebuild trust in information.