Connect AI to Billions of Legal Documents — Simon Eskildsen, turbopuffer & Jacob Lauritzen, Legora

Scaling search infrastructure for legal AI platforms requires balancing massive document volumes with strict enterprise demands for data residency and physical isolation. Legora initially struggled with Elasticsearch and Postgres-based solutions, which suffered from high latency and operational overhead when managing thousands of tenants. Transitioning to TurboPuffer enabled a shift to an object-storage-native architecture, utilizing namespace-based isolation to satisfy security requirements like customer-managed encryption keys. By leveraging a tiered memory hierarchy—efficiently moving data between DRAM, NVMe, and S3—TurboPuffer delivers order-of-magnitude latency improvements and cost efficiency for both active project search and large-scale legal research. This architectural evolution allows engineering teams to move away from infrastructure maintenance and focus on product innovation, effectively handling billions of vectors while maintaining high performance for diverse, multi-tenant workloads.