How Lawme Scaled AI Legal Assistants and Significantly Cut Costs with Qdrant - Qdrant

This case study details Lawme's migration from PGVector to Qdrant to scale their AI legal assistants. Key technical contributions include the successful implementation of Qdrant's binary quantization for efficient retrieval from tens of millions of legal vectors, and the utilization of metadata filtering with Qdrant's HNSW index to quickly filter queries by jurisdiction or case type. The post highlights the benefits of Qdrant's flexible deployment options, enabling Lawme to meet strict data residency and compliance requirements for legal clients.