Pranav Aurora

Traditional search breaks under the bursty, concurrent demands of multi-step agents. In this session, learn how to build agent-native search on Postgres that extends pgvector with advanced indexing and first-class BM25, with vector and full-text indexes living right alongside your operational data. The payoff: no brittle app-level joins, just single-statement SQL that fuses vector rankings with structured filters. Watch it scale to zero when idle, burst to billion-vector scale with no performance hit, and branch instantly for sandboxing and A/B testing, all without rebuilding a single index. Then get a first look at what we are building to make Lakebase the foundation for the next generation of AI agents.

The medallion architecture dictates how we typically think about data, but that framework has historically been limited to the data lifecycle strictly within the Lakehouse. Lakebase takes this a step further by expanding the architectural blueprint to redefine how data enters your Bronze layer and how it is consumed from Gold. Rather than extracting and moving data across system boundaries, your operational database natively acts as the Bronze foundation to immediately feed downstream analytics. On the other end, Lakebase closes the loop by directly serving those refined Gold datasets to production applications with operational low latency, eliminating the need to export data out of your ecosystem.