{"id":15219,"date":"2024-07-10T09:20:41","date_gmt":"2024-07-10T01:20:41","guid":{"rendered":"https:\/\/www.1ai.net\/?p=15219"},"modified":"2024-07-10T09:20:41","modified_gmt":"2024-07-10T01:20:41","slug":"hebbia-%e8%8e%b7%e5%be%97-1-3-%e4%ba%bf%e7%be%8e%e5%85%83%e8%9e%8d%e8%b5%84%ef%bc%8c%e6%89%93%e9%80%a0-ai-%e7%9f%a5%e8%af%86%e6%a3%80%e7%b4%a2%e5%b9%b3%e5%8f%b0","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/15219.html","title":{"rendered":"Hebbia receives $130 million in funding to build an AI knowledge retrieval platform"},"content":{"rendered":"

New York-based Hebbia announced a $130 million Series BFinancing<\/a>, with investors including Andreessen Horowitz, Index Ventures, Peter Thiel and Google's venture capital arm.<\/p>\n

What Hebbia is building is a fairly simple thing: a LLM<\/a> A localized productivity interface that makes it easier to derive value from data, regardless of its type or size. The company has already partnered with some of the biggest names in the financial services industry, including hedge funds and investment banks, and plans to bring the technology to more organizations in the coming days.<\/p>\n

\"\"<\/p>\n

Product Portal: https:\/\/top.aibase.com\/tool\/hebbia<\/p>\n

Although the LLM-basedChatbots<\/a>This can be based on internal documents or prompted documents, but many have noted that these helpers are unable to answer complex questions about business functions. In some cases, the problem is the context window, which can't handle the size of the provided documentation, while in other cases, the complexity of the query prevents the model from solving it accurately. Errors may even affect the team's confidence in the language model.<\/p>\n

Hebbia addresses this gap by providing LLM-related proxy co-pilot Matrix. This product is located in the business environment of the company and allows knowledge workers to raise complex issues related to internal documentation \u2014 from PDF, spreadsheets and Word documents to audio transfer \u2014 with unlimited context windows\u3002<\/p>\n

Once the user provides the query and associated documents\/files, Matrix breaks it down into smaller operations that the LLM can perform. This makes it possible to analyze all the information contained in a document at once and extract what is needed in a structured form. hebbia says the platform enables the model to reason about any number (millions to billions of documents) and modality of data while providing relevant references to help the user keep track of each operation and understand how the platform ultimately arrives at an answer.<\/p>\n

With its latest round of funding, the company hopes to build on this and attract more large organizations to its platform to streamline the way their staff retrieve knowledge.<\/p>\n

Hebbia isn't the only company in this space. Others are exploring AI-based knowledge retrieval for the enterprise, including Glean. the Palo Alto, California-based startup reached unicorn status in 2022 and is building a ChatGPT-like assistant specifically for workplace productivity. There are also players like Vectara that are working to enable a universal AI experience based on enterprise data.<\/p>","protected":false},"excerpt":{"rendered":"

New York-based Hebbia has announced a $130 million Series B funding round from Andreessen Horowitz, Index Ventures, Peter Thiel and Google's venture capital arm. What Hebbia is building is something fairly simple: an LLM-native productivity interface that makes it easier to derive value from data, regardless of type or size. The company is already working with some of the biggest names in the financial services industry, including hedge funds and investment banks, and plans to bring the technology to more organizations in the coming days. Product Portal: https:\/\/top.aibase.com\/tool\/hebbia though<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[3425,473,275,421],"collection":[],"class_list":["post-15219","post","type-post","status-publish","format-standard","hentry","category-news","tag-hebbia","tag-llm","tag-275","tag-421"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/15219","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=15219"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/15219\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=15219"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=15219"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=15219"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=15219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}