Daniel Wrigley
Daniel has worked in search since graduating in computational linguistics studies at Ludwig-Maximilians-University Munich in 2012 where he developed his weakness for search and natural language processing. His experience as a search consultant paved the way for becoming an O’Reilly author co-authoring the first German book on Apache Solr.
His current work focuses on leveraging AI agents to accelerate the next generation of search quality improvements.
In his free time he supports the local fire brigade as a volunteer firefighter and serves as the sports director of the local shooting club in the village he lives in.
OpenSource Connections
Session
Search relevance tuning is notoriously difficult, often requiring a deep understanding of Lucene scoring, complex query DSLs, and iterative manual testing.
This session introduces Agentic Relevance Tuning—a framework that leverages LLM-based agents to automate the full search lifecycle making search tuning faster, more accurate, and accessible.