Vector Search Solutions

Challenges and tasks solved with Qdrant

Find similar images, detect duplicates, or even find a picture by text description - all of that you can do with Qdrant vector database. Start with pre-trained models and fine-tune them for better accuracy. Check out our demo!

The vector search uses semantic embeddings instead of keywords and works best with short texts. With Qdrant, you can build and deploy semantic neural search on your data in minutes. Check out our demo!


User behavior can be represented as a semantic vector in a similar way as text or images. Vector database allows you to create a real-time recommendation engine. No MapReduce cluster required.

Semantic search for intent detection is the key chatbot technology. In combination with conversation scripts, modern NLP models and Qdrant, it is possible to build an automated FAQ answering system.

Matching Engines

Matching semantically complex objects is a special case of search. Usually a large number of additional conditions are used in matching, which makes Qdrant an ideal tool for building such systems.

Anomaly detection is one of the non-obvious applications of Similarity Learning. However, it has a number of properties that make it an excellent way to approach anomaly detection.

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