In the era of artificial intelligence (AI), enterprises are eager to leverage large language models (LLMs) to optimize critical internal functions. Despite significant investments, achieving a substantial return on investment (ROI) remains a challenge. Today, New York-based startup Hebbia, which focuses on simplifying information retrieval through AI, announced it has secured $130 million in Series B funding from notable investors including Andreessen Horowitz, Index Ventures, Peter Thiel, and the venture capital arm of Google.

Hebbia is developing a straightforward yet powerful LLM-native productivity interface that streamlines the extraction of value from data, regardless of its type or size. The company is already collaborating with major financial services firms, including hedge funds and investment banks, and plans to extend its technology to more enterprises soon.

“AI is undoubtedly the most important technology of our lives. But technology doesn’t drive revolutions– products do. Hebbia is building the human layer – the product layer – to AI,” George Sivulka, the founder and CEO of Hebbia, stated in a blog post. Prior to this funding round, the company raised $31 million through several rounds.

Hebbia’s Offerings and Technological Innovations

While LLM-based chatbots can utilize internal documentation or be prompted with documents, they often fail to answer complex questions about business functions accurately. This can be due to limitations in the context window, which may not handle the size of the document provided, or the sheer complexity of the query itself. Such errors can erode teams’ confidence in the capabilities of language models.

Hebbia, founded in 2020, addresses these challenges with its LLM-linked agentic copilot called Matrix. This tool operates within the business environment of companies, enabling knowledge workers to pose intricate questions related to internal documents—ranging from PDFs and spreadsheets to audio transcripts—with an infinite context window.

The Matrix platform allows users to input queries and associated documents/files. The system then decomposes the prompt into smaller, manageable actions that the underlying LLM can execute. This process enables the platform to analyze all the information contained in the documents simultaneously and extract the necessary data in a structured format. Hebbia claims that its platform can process and reason over any volume (from millions to billions of documents) and modality of data, while also providing relevant citations to help users trace each action and understand the platform’s decision-making process.

“Designed for the knowledge worker, Hebbia lets you instruct AI agents to complete tasks exactly the way you do them – no task too complex, no dataset too large, and with the full flexibility and transparency of a spreadsheet (or a human analyst!),” Sivulka elaborated in the blog post.

Key Features of Hebbia’s Matrix Platform

• Infinite Context Window: Handles extensive volumes of data, making it ideal for enterprises with vast documentation.
• Comprehensive Data Analysis: Capable of processing various data formats simultaneously.
• Actionable Intelligence: Breaks down complex queries into manageable tasks for accurate results.
• Transparency and Traceability: Provides citations for every action, ensuring transparency in the decision-making process.
• Versatility: Adapts to various industries, including financial services and beyond.

Significant Impact and Growth Trajectory

Initially, Sivulka created the platform to streamline the workload of financial industry workers who spent substantial time sifting through documents for relevant information. Over the years, Hebbia has expanded its reach, gaining traction in other sectors as well. Currently, the company boasts over 1,000 use cases in production with several major enterprises, including CharlesBank, American Industrial Partners, Oak Hill Advisors, Center View Partners, Fisher Phillips, and the U.S. Air Force.

“Over the last 18 months, we grew revenue 15X, quintupled headcount, drove over 2% of OpenAI’s daily volume, and laid the groundwork for customers to redefine how they work,” Sivulka noted. It remains uncertain whether OpenAI is the sole model used within the Matrix platform or if users have the option to select other LLMs.

With the recent funding, Hebbia aims to build on its successes and attract more large enterprises to its platform, simplifying how their workers retrieve knowledge. “I’m excited for a world of unbound progress– one where AI agents contribute more to global GDP than every human employee. I believe that Hebbia is going to get us there,” Sivulka added, highlighting that the company is developing what he considers the most important software product of the next 100 years.

Competitive Landscape

Despite its innovative approach, Hebbia is not alone in the AI-based knowledge retrieval space. Other companies are also exploring similar technologies. For instance, Glean, a Palo Alto-based startup, achieved unicorn status in 2022 and has developed a ChatGPT-like assistant specifically for workplace productivity. Additionally, Vectara is working on enabling generative AI experiences grounded in enterprise data.

Future Outlook

Hebbia’s recent funding round and its expanding list of enterprise clients underscore its potential to significantly impact the field of AI-driven knowledge retrieval. As the company continues to innovate and enhance its platform, it is well-positioned to lead the market and drive substantial improvements in how businesses manage and utilize their data.

With its focus on creating practical, product-driven AI solutions, Hebbia is poised to help enterprises navigate the complexities of data management and retrieval, ultimately unlocking new levels of efficiency and productivity.