Case Study: Cypris achieves report generation in minutes and scalable vector search with Elastic

Cypris puts Elastic vector search and RAG technologies at the heart of its AI-powered research platform

Cypris is an AI-driven research platform for R&D teams that needed to speed up time-consuming manual analysis of patents, papers, and news while meeting strict security and scalability requirements for enterprise and government clients. Facing slow report generation, high in-house development costs, and reliability issues with a prior provider, Cypris sought a semantic search and retrieval solution that could handle massive, constantly updated datasets.

Cypris integrated Elasticsearch’s native vector search and retrieval-augmented generation (RAG) with generative AI, using dense vectors and hybrid vector/BM25 queries plus Elastic’s support to accelerate development. The platform now delivers detailed reports in about 15 minutes (vs. weeks), reduced development costs and time-to-market, scales over 500 million documents (10+ TB) with robust reliability, has driven ~30% quarterly customer growth, and helped win high-profile government contracts and multi-million-dollar investments.

Cypris

Logan Pashby

Principal Engineer