.NET Day Switzerland 2025 - Supercharged Search with Semantic Search and Vector Embeddings
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.
Join me for a demo-rich session and learn how to implement semantic search in .NET with Azure SQL Database and Entity Framework Core.
Zurich, Switzerland
I’m a backend engineer and software architect with 17+ years of .NET, mostly in banking and fintech: payment and transaction processing, public APIs, and the internal platforms other teams build on. I’ve led teams and a platform unit, and I stay hands-on in the code, increasingly through AI coding agents like Claude Code and Codex.
Alongside that, I build open-source .NET libraries and developer tools with 45M+ NuGet downloads: DuckDB.NET, EntityFramework.Exceptions, EFCore.Visualizer and others. That work sits where .NET meets the database: native interop, Native AOT extensions for DuckDB and PostgreSQL, and tooling that makes query plans easier to debug. I also speak about .NET and databases at conferences like NDC, KCDC and .NET Conf.