Qdrant, a Germany-based software development startup, has raised an impressive $7.5 million in seed funding led by Unusual Ventures, 42CAP, and IBB Ventures. Founded by Andre Zayarni, Qdrant provides the next generation of AI applications with advanced and high-performant vector similarity search technology through its powerful vector database. With Qdrant, unstructured data can be effectively utilized, allowing users to deploy embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and much more! The Qdrant API is easy to use and provides the OpenAPI v3 specification to generate a client library in almost any programming language. 

Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved. Qdrant's vector payload also supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Furthermore, it is cloud-native and scales horizontally, making it efficient and effective in utilizing resources. Qdrant's unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search ensures search with state-of-the-art speed and accuracy. The startup's strengths lie in its ability to solve various challenges and tasks, including semantic text search, recommendations, and similar image searches.