How Bayer Built an Enterprise-Scale Search Engine with Qdrant

Bayer developed myGenAssist, an internal generative AI platform that uses Qdrant as a core retrieval layer to serve 116,000 employees, process more than 1.5 million monthly messages, and index hundreds of thousands of uploaded documents. After initially testing Redis, the company chose Qdrant for its open-source availability, latency, performance characteristics, and ability to support compliance through a hybrid-cloud deployment that retains data in Bayer-controlled infrastructure while reducing operational overhead. Its four-node cluster stores roughly 135 million points across collections supporting hybrid dense and sparse search, agent memory, enterprise tool discovery, document search, and a growing multimodal system for text, images, audio, video, and scientific data. Bayer’s agents can access Qdrant directly to select keyword, semantic, or hybrid retrieval strategies, while query expansion, reranking, tenant isolation, quantization, lazy document parsing, and semantic caching aim to improve relevance, manage costs, and maintain responsive performance. The platform also provides citations, traceability, and monitoring intended to meet life-sciences compliance needs, and Bayer reports an approximately 20% efficiency gain from AI use while expanding toward agent-assisted drug discovery, chemical research, and regulatory workflows.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.