{"id":167924,"date":"2023-04-24T17:16:02","date_gmt":"2023-04-24T16:16:02","guid":{"rendered":"https:\/\/liora.io\/en\/?p=167924"},"modified":"2026-02-06T09:04:05","modified_gmt":"2026-02-06T08:04:05","slug":"50-million-raised-to-develop-the-next-vector-databases","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/50-million-raised-to-develop-the-next-vector-databases","title":{"rendered":"50 million raised to develop the next vector databases"},"content":{"rendered":"

To answer the questions correctly they\u2019re asked, chatbots store data as vectors. This method allows artificial intelligence to understand the relationship between grouped information like the words dog and cat. To improve generative AI, Weaviate received $50 million to develop its vector database solution.<\/strong><\/p>\n

What is Weaviate\u2019s solution?<\/h3>\nWeaviate<\/a> is an Amsterdam-based company<\/b> producing open source databases<\/a> designed to store vectors for AI systems. A vector is a mathematical structure<\/b> that groups different types of information together. \n\nVectors can include documents, word banks and other types of information. Thanks to this grouped information, algorithms are better able to understand the relationship<\/b> between different words such as dog, cat, human. In cybersecurity, people used this similarity technique often<\/b> to identify known malware drifts. \n\nWeaviate\u2019s solution allows storing billions of vectors<\/b> and modulating them easily<\/b>. Besides the storage offered, the company lets its modulation software be used<\/b> by companies with specialized needs. Access to the company\u2019s databases is through a GraphQL API<\/b> and helps reduce data errors and optimize retrieval performance.\n\n\"\"\n

What will the $50 million be used for?<\/h3>\nIndex Ventures<\/b>, a US-based venture capital firm specializing in technology investments, completed this round of funding. Also involved in this round of funding are Battery Ventures, NEA, Cortical Ventures, Zetta Ventures and ING Ventures. This Series B round brings Weaviat\u2019’s total funding to $67.7 million<\/b>.\n\nWith this funding, the company announced its hiring of new data science engineers<\/a> and developing its product<\/b>. In a recent report, the company said two million people<\/b> have downloaded its database solution. Weaviate CEO, Bob van Luijt,<\/b> marks the capital as a boon in a rhythmic time for generative AI.\n\n\"\"\n\nWith the development of generative AI, new use cases and performance solutions are also being created<\/b>. This drive for robotic authoring technology is forming a tree of tools<\/b> and ancillary software enjoying this boom. One tool that has been in the news a lot recently is AutoGPT, a software that can automate ChatGPT<\/a>. That’s why if you liked this article and if you’re considering a career in Data Science, don’t hesitate to discover our articles<\/a> or our training offers<\/a> on Liora.\n\nSource: weaviate.io<\/i>\n\n\n
Discover our training courses<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

To answer the questions correctly they\u2019re asked, chatbots store data as vectors. This method allows artificial intelligence to understand the relationship between grouped information like the words dog and cat. To improve generative AI, Weaviate received $50 million to develop its vector database solution. What is Weaviate\u2019s solution? Weaviate is an Amsterdam-based company producing open […]<\/p>\n","protected":false},"author":74,"featured_media":167927,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-167924","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167924","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=167924"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167924\/revisions"}],"predecessor-version":[{"id":206421,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167924\/revisions\/206421"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/167927"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=167924"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=167924"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}