Qdrant Powers Sapu AI Platform Indexing 28 Million PubMed Abstracts to Accelerate Cancer Research

Qdrant, a provider of vector search technology, announced that its Qdrant Cloud infrastructure powers the AI research platform of Sapu, an early-stage biopharmaceutical company developing treatments for hard-to-treat cancers. The platform indexes and queries all 28 million PubMed abstracts in a single searchable collection, significantly accelerating biomedical discovery workflows, according to a blog post by Daniel Azoulai.

According to the post, Sapu's AI platform evolved from an early prototype into a production-scale system supporting scientific literature review, standard operating procedure retrieval, and AI-assisted research authorship. The company stated that the platform has already contributed to seven peer-reviewed research papers while being used broadly across its research operations.

The blog also noted that Sapu is expanding the platform's capabilities through a robotics partnership with Techforce and evaluating edge deployments for secure, air-gapped laboratory environments. Qdrant's hybrid vector and metadata retrieval architecture is central to enabling the scale, speed, and flexibility required for those next-stage applications.

Qdrant is a developer-focused provider of vector search technology built to power AI applications at scale. Founded after co-founders André Zayarni and Andrey Vasnetsov identified a gap in existing vector similarity search tools, the company developed a production-ready vector search engine designed to deliver the scalability, performance, and feature set needed for real-world AI and machine learning deployments. What began as an open-source GitHub project has grown into an enterprise-grade platform supporting startups and large-scale organizations alike. Qdrant offers both open-source and managed cloud vector search solutions, giving developers precise control over indexing, search, and retrieval of high-dimensional data. Built in Rust, Qdrant has surpassed 250 million downloads, earned more than 29,000 GitHub stars, and grown to a global team of more than 100 employees across 20-plus countries, focused on advancing scalable infrastructure for next-generation AI applications.

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