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Venture Capital News: eBrevia Raises $1.5M Seed Round

2014-11-26
STAMFORD, CT, Leading enterprise solution for automated contract review and analytics announces today that the company has raised $1.5 million dollars in seed funding.
eBrevia (http://ebrevia.com), the leading enterprise solution for automated contract review and analytics announces today that the company has raised $1.5 million dollars in seed funding, bringing the company's total funding to $2.1 million. Connecticut Innovations, the leading source of financing and ongoing support for Connecticut's innovative, growing companies, led the round, with participation from private investors.
Founded by Harvard Law School graduates Ned Gannon and Adam Nguyen and computer scientist Jacob Mundt in 2011, eBrevia's machine learning technology brings greater accuracy and efficiency to contract review and analytics.
In the $93 billion corporate law market, junior attorneys are routinely billed at approximately $300-500 per hour to perform contract review in due diligence. The legal fees associated with conducting due diligence in mergers and acquisitions alone are approximately $1.5 billion annually. By adding eBrevia's machine learning technology to enhance the current manual process, corporate lawyers, in-house law departments, financial institutions and commercial real estate companies can increase speed and decrease costs by at least 50 percent. In addition to transaction-based applications, companies can also use eBrevia's software to extract and track obligations in their existing contracts.
'Contract review is a time-intensive and costly process, yet it remains of utmost importance in due diligence for mergers and acquisitions as well as financial and commercial real estate transactions,' said Ned Gannon, CEO of eBrevia. 'Our artificial intelligence technology reviews, extracts and summarizes key legal provisions and other information in contracts. The machine learning software has been trained on thousands of publicly available contracts used in hundreds of past transactions. By adding our technology to the manual process of contract review, law firms, in-house legal departments, financial institutions and commercial real estate companies can expect to see faster and more accurate results.'
eBrevia's technology was incubated in the Computer Science department of Columbia University's Fu Foundation School of Engineering and Applied Sciences and the company remains a portfolio company of Columbia Technology Ventures, the technology transfer arm of Columbia University. 'eBrevia's core machine learning technology is truly revolutionary,' said Professor Kathy McKeown, Director of the Data Science Institute at Columbia University. 'It's also a fine example of how Columbia's research on automatic summarization of data is being applied to transform the contract review function in law and financial services. We are very proud to be aligned with eBrevia as they continue to establish themselves as trailblazers in this new way of doing work.'
McKeown is on eBrevia's Board of Advisors, which also includes Andy Shane, Managing Director of nSource and former head of Strategy and Business Development at Bloomberg Law, and Jeff Munsie, General Counsel at Merrimack Pharmaceuticals, Inc.
For more information about eBrevia, click here: http://www.ebrevia.com
About eBrevia
Based on technology developed at Columbia University, eBrevia provides leading enterprise contract review and analysis solutions, leveraging machine learning to produce faster and more accurate results. The software, which extracts and summarizes key legal provisions and other information, can be used in due diligence, contract management, lease abstraction, and document drafting. Based in Stamford, CT, eBrevia was founded in 2011 by Harvard Law graduates Ned Gannon and Adam Nguyen and computer scientist Jacob Mundt. The company has raised $2.1 million in seed funding and has offices in Stamford and New York City. eBrevia was one of four national winners in the Startup America DEMO Competition and received the Connecticut Technology Council's Most Promising Software Product of the Year award.
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