November 16, 2020 by Zilliz
SHANGHAI, Nov. 13, 2020 – AI-powered unstructured data processing and analysis startup Zilliz closed its latest Series B funding round with a $43 million investment led by Hillhouse Capital. Additional investors include TBP Capital as well as continued participation from existing investors Morningside Venture Capital and Yunqi Partners. This marks the largest single Series B funding round for an open-source infrastructure software company to date.
Zilliz has raised over $53 million since its 2017 launch, and will use the latest round of funding to drive international workforce expansion, sustain and expand contributions to the open-source data science software ecosystem, advance development of enterprise-grade cloud services, and spur global adoption of AI-powered technology for unstructured data processing and analysis.
Unstructured data accounts for roughly 80% of all data, but only 1% gets analyzed due to processing complexities,” said Charles Xie, Founder and CEO of Zilliz. “Structured data, such as numerical values and text, has been efficiently handled by computers for decades. Unstructured data, things like images, videos, user behavior, chemical structures, and gene sequences, is far more ubiquitous. However, few tools are available to extract meaning from it. As digital transformation accelerates, unlocking the value of unstructured data will become progressively more essential. Our overarching goal at Zilliz is to utilize technology like heterogeneous computing and artificial intelligence to give people access to previously inaccessible insights.”
Xie continued, “Unstructured data processing and analysis has been pivotal to growing adoption of artificial intelligence worldwide, and we are committed to sustained investment in this space. We’ve found building software under the open-source model has eliminated national boundaries, brought a powerful network effect, and made analyzing unstructured data easier and more accessible for developers everywhere. Beginning late 2021, Zilliz will provide public, cloud-based unstructured data services that emphasize security, reliability, ease of use, and affordability."
Additional comments on the investment came from stakeholders at Hillhouse Capital, Morningside Venture Captial, and Yunqi Partners:
Liming Huang, Investment Director, Technology Sector at Hillhouse Capital said, “How humans interact with each other, and our environments, is too complex to be represented by structured data. Hillhouse firmly believes leveraging embeddings similarity search to analyze massive unstructured datasets is the key to solving existing big data challenges. With Charles at the helm, Zilliz is building versatile applications that are a huge boon to the open-source data science software space and represent the future of data search.”
Levi Liu, Partner at Morningside Venture Capital commented, “Three years ago Charles began ardently laying out his vision for meeting data processing demands in an increasingly digital future. We were compelled by the wide-array of real-world scenarios unstructured data analysis benefits, and have been repeatedly impressed by the innovation Zilliz is bringing to a variety of domains. We view this investment as the continuation of a long-term partnership.”
Yu Chen, Managing Director at Yunqi Partners said, “Deep knowledge of database management, computer architecture, and the open-source software model make Charles an ideal fit for pioneering vector search technology. Zilliz’s product lines excel in almost every aspect including speed, scalability, and ease of use. We remain optimistic about future product developments, as well as the company’s ongoing contributions to global open-source communities.”
Zilliz was founded in 2017 and operates as a distributed team with offices in both the U.S. and China. The company specializes in the development of open-source, AI-powered unstructured data analysis software, and is the initiator and primary contributor to the vector similarity search project Milvus.
Milvus is currently an incubation-stage project at the LF AI Foundation, has been adopted by 400+ enterprise users, and has applications spanning image processing, computer vision, natural language processing (NLP), speech recognition, recommendation engines, search engines, new drug development, gene analysis, and more.
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