Case Study: &AI scales billion-vector legal retrieval with Qdrant

&AI scales global legal retrieval to over 1 billion vectors with Qdrant

The customer, &AI, is a legal tech company on a mission to redefine patent litigation. Their challenge was to power fast, accurate retrieval across billions of patent documents for their platform, which helps legal professionals invalidate patents. This required a robust, cloud-native vector search solution that could handle immense scale without increasing DevOps complexity. They chose Qdrant Cloud to meet this need.

The solution implemented by Qdrant enabled &AI to scale to over a billion vectors while maintaining sub-second latency search times. Using Qdrant's features like filterable HNSW, payload indexing, multitenancy, and scalar quantization, &AI built a retrieval-first legal AI stack. This resulted in the ability to power workflows that used to take hours in just a few minutes, drastically cutting time-to-result for attorneys and allowing the company to offer strong guarantees of comprehensiveness to its clients.

&AI