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Legal AI tool to fill ‘gap in the market’

A new artificial intelligence tool has been launched to the legal profession, which promises to fill the “gap in the market” left by other platforms of this kind.

user iconEmma Musgrave 19 January 2017 Corporate Counsel
Legal AI tool to fill ‘gap in the market’
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Provider of outsourced legal and technology services, Exigent, has unveiled the new service, which it says allows legal departments to leverage machine learning technology for fast and effective discovery and processing of contract and policy data.

Exigent CEO David Holme said the new platform fills a vital gap in the legal market. He noted that it's different from any other tool out there “because it is fully customisable, meaning that even companies with messy contract folders can use it”.

“We weren’t interested in offering just another contract discovery tool, but in harnessing the power of machine learning to make contracts the cornerstone of strategic business decisions. People or technology alone are not the answer, but bundled together and as part of a broader value-based strategy, they can deliver a real shift in how the legal department works,” Mr Holme said.

“We realised there was a gap in the market: all of these AI tools rely on companies having a good, structured approach to contract management. The reality is that most organisations don’t even know where their contracts are and they find themselves stuck with technology that only works with a rigid set of rules that doesn’t meet their business needs.


“We offer companies the opportunity to start sorting out their contracts in a quick and sophisticated way, using both technology and clever lawyers to enhance the process. Once an organisation has carried out solid contract discovery, it can set out a data strategy and apply analytics to make better, more informed business decisions.”

Exigent partnered with Chicago-headquartered tech company LexPredict to roll out the new AI platform.

“The new technology is supported by lawyers and analysts trained in machine learning and natural language processing, making the service a scalable, modular option, with no need to commence large scale IT projects or hiring external resources,” explained LexPredict CEO Michael Bommarito.

“Material of any type, including emails, is fed to the system, which sorts them automatically and identifies legally relevant items and standard clauses.

“[Users] then train the system to recognise and classify custom or complex clauses, moving away from a simple yes/no binary identification so typical of other machine learning products on the market.”

Mr Bommarito noted that the new system is unique as it encourages “man and machine” to work together, rather than as one entity.

“Our driving vision as a firm is that man and machine together are better than either alone, and we’re very excited to be working with the men and women at Exigent to bring this vision to legal departments around the world,” he said.

“While many vendors sell licenses for fragile ‘artificial intelligence’ that frankly does not exist, we are focused on delivering solutions to pressing business problems using the best blend of resources and technology possible.

“One of the many use cases for Exigent’s machine learning service is the due diligence process in an M&A transaction. Exigent’s approach starts by searching through every document and communication, including emails, to identify relevant documents such as traditional agreements or policy and procedures. Every clause, term and detail is then tagged and made searchable, enabling the legal team to find the anomalies and exceptions that are likely to present a greater risk than the standard clauses, more efficiently.”

Mr Bommarito added that the system saves time and helps deals to progress more quickly, while removing the likelihood of deal fatigue.

“In addition to that, aggregating data means that when deals with suppliers require renegotiation or when license agreements are due to expire, the legal team is alerted and new, better value contracts drawn up,” he concluded.

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