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Researchers and scientists around the world are using graphs to tackle real-world problems, from societal networks to protein interactions to communication networks to eCommerce user behavior analysis. Lots of mathematical and statistical operations can be better done in embedded graphs that are presented in vector spaces. Ultipa naturally supports graph embeddings and varied neural networks, we are fast, intuitive and convenient, eliminating the needs to rely on multiple siloed systems and cumbersome data ETL tasks.

  • Pain Points
  • Slowness

    Preparing and embedding graph data is usually a lengthy and complex process, because the underpinning graph system is not designed to tackle such tasks effectively with efficacy.

    Great Performance

    Ultipa Graph offers far superior performance to other databases, often times 10x-to-100x faster.

  • Siloed Systems

    It usually requires multiple siloed systems to work together, lots of ETL on multiple datasets are often involved, having one unified system to tackle graph embedding is highly desirable.


    Imagine not having to rely on multiple IT systems like Spark + Neo4j + Tensorflow for your AI/graph training. With Ultipa, graph embedding & learning can be done in one central place with simplicity and ease!

  • Black-Box

    AI is going more and more sophisticated and un-explainable, in short, black-box to human beings, this is unacceptable and potentially a major risk to the well-being of human species.


    Graph has the absolute potential to fix this by white-box AI.