Tasks and Problems solved with Qdrant

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

Find similar images, detect duplicates, or even find a picture by text description - all of that you can do with Qdrant. Mostly you won’t even need to train a neural network for that. Pre-trained models are usually enough to begin with. Check out our demo!

User behavior can be represented as a semantic vector is similar way as text or images. Qdrant allows you to create a recommendation engine with custom filters and real-time updates. No need to deploy a MapReduce cluster.

Semantic search for similar phrases is one of the key technologies for building chatbots. In combination with conversation scripts, modern pre-trained NLP models and Qdrant, it is possible to build an automated FAQ answering system.

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 Metric Learning. However, Metric Learning has a number of properties that make it an excellent way to approach anomaly detection. Check out our case-study!