As a moderately active angel investor, I spend a lot of time reviewing legal documents. It’s a long, slow, laborious process and one which on the one hand introduces significant risks (what if I miss something important?) while on the other adding almost zero value to a transaction (after all, hardly anyone will ever refer to these documents again). So I’m always interested to hear about ideas for making the document review process simpler.
Recently I came across eBrevia, a young startup founded by a couple of Harvard Law school graduates. Ned Gannon and Adam Nguyen had spent time in commercial law offices and had seen just how much time junior associates spend reviewing contracts and documents – especially in the case of complex merger and acquisition deals. Often legal practices will charge flat fees for this work, and often there is push back from clients about the charging of junior associates’ time for this sort of work. For these reasons legal practices are looking for ways to make the document review process far more efficient.
Gannon and Nguyen were aware of some machine learning technology that was based upon research out of Columbia University. They licensed the technology and created eBrevia, a kind of e-discovery tool for the document review process. eBrevia uses machine learning to determine what is relevant to attorneys in contracts. It then extracts this relevant information for review by attorneys. What this means is that in a very long and complex legal document, eBrevia will help attorneys find relevant information – perhaps it is a merger and acquisition situation and they're looking for any clauses which dictates what happens to a contract upon change of control, eBrevia can find that information, no matter how deeply it is buried in convoluted clauses, and extract it for attorney review. eBrevia ensures that stuff doesn’t fall through the cracks, it helps practices become faster and more efficient, and helps attorneys deliver more accurate work. Instead of mid-level associates having to check the work of entry level staff, the platform does the low-level work automatically.
In terms of use, eBrevia provides a data room. Users can upload documents and eBrevia will process them, or in the case of scanned documents, eBrevia can digitize them via Optical Character Recognition (OCR) and thereafter process the documents. The user selects a specific provision type (change of control for example) and the platform extracts all the relevant clauses relating to that provision type.
Documents can then be edited and published - attorneys who represent the buyer can get a good understanding of the legal obligations of the seller. The buyer goes through contract and related documents to assess risk (ie change of control means a contract changes, employment non-compete on change of control etc) thereby surfacing relevant issue. eBrevia is also being used by in-house legal departments as a contract management tool – eBrevia can extract provisions to make sure that an organization isn’t paying for services that they don’t actually need to.
Recently eBrevia added another vertical offering - one to be used with leasing documents. eBrevia’s Lease Abstractor will leverage the company’s existing technology to bring greater accuracy and efficiency to the lease abstraction process in commercial real estate firms and law firm real estate departments. Talking to this opportunity, eBravia says that:
Lease abstraction and administration is critical to commercial real estate firms, yet the process has historically proven to be extraordinarily time and labor intensive. This process, whether performed in-house or outsourced, consists of reviewers reading leases and manually extracting data, costing property management organizations fees in excess of hundreds of dollars per lease. eBrevia’s solution was designed to augment the current manual process. The company’s Lease Abstractor product uses machine learning to analyze the lease and extract relevant information based on user specifications. By leveraging eBrevia’s artificial intelligence technology, commercial real estate companies can significantly increase speed and decrease costs associated with lease review and administration.
eBrevia was spun out from Columbia in 2012, with the University giving eBrevia exclusive license to use the core machine learning technology. eBrevia closed a $1.5 million funding round late last year and currently has a handful of employees. Despite their small size, eBrevia has seen adoption on both sides of the Atlantic, with some global legal firms using the solution.
In terms of competition, there are many eDiscovery tools, but away from the litigation side of the legal industry, there is little application of these sorts of tools in the transactional part of the industry. I love what eBrevia is doing and look forward to seeing their progress – traditional legal service and document providers like CCH and Lexis Nexis will no doubt watch this company with interest.
