Open-source database startup Weaviate B.V. today announced that it has secured a $50 million investment led by Index Ventures.

Battery Ventures participated as well along with returning backers NEA, Cortical Ventures, Zetta Venture Partners and ING Ventures. The Series B round brings Weaviate’s total outside funding to $67.7 million. The startup will use the new capital to hire more employees, as well speed up product development initiatives.

Artificial intelligence systems such as chatbots store the data they use to answer questions in the form of vectors. A vector is a mathematical structure that can represent documents, purchase logs and many other types of information. Storing records in this form makes it easier for AI models to understand their meaning.

Amsterdam-based Weaviate offers an open-source database built specifically to store AI models’ vectors. The database, which is also called Weaviate, can store up to billions of vectors. Moreover, it makes them easier to process.

Turning raw data such as business documents into vectors requires developers to use specialized algorithms. Weaviate includes a prebuilt vectorization module that promises to ease the task. Moreover, the startup’s database enables companies with advanced requirements to use their own custom vectorization software if necessary.

Weaviate groups the records it ingests based on similarity. If the database receives two sets of academic papers that discuss processors and memory chips, respectively, it can organize the papers by topic into two separate collections. It takes the same approach with other types of information.

According to the startup, its database groups records by similarity using an approach that draws on the field of geometry. Each file that Weaviate stores is represented as a point in space. Points representing similar files are placed close to one another, while dissimilar data points are farther apart.

The ability to identify whether two records are similar is vital to many AI use cases. AI-powered cybersecurity tools, for example, determine whether a file might be malicious by checking if it’s similar to known malware strands. Weaviate says that its platform can likewise ease data classification, a term that covers tasks such as organizing business documents by topic.

Another use case that the startup promises to simplify is search. Developers can use its database to build AI-powered search applications that rapidly sift through large amounts of information. The startup is promising subsecond query processing times in some cases.

The database makes records accessible through GraphQL application programming interfaces. GraphQL is a technology that allows applications to more efficiently retrieve information from databases. It can improve performance, as well as reduce the risk of data errors. 

“The Weaviate vector database is used as core infrastructure in the emerging AI-native ecosystem,” said co-founder and Chief Executive Officer Bob van Luijt “It allows users, from startups to enterprises, to create a new wave of applications ranging from custom-made search and recommendation systems to ChatGPT plugins.”

The startup says that the open-source version of its database has been downloaded more than 2 million times. To further grow its installed base, Weaviate will use a part of its newly raised funding to accelerate go-to-market efforts. The investment comes a few weeks after Chroma Inc., another startup with an open-source database optimized for AI applications, raised a $16 million funding round of its own. 

Image: Weaviate