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Tuesday, March 10, 2020

AI and Legal Discovery

More developments for the textually and logic intense world of Law.  How much of legal tasks be replaced by systems? Following this.

Everlaw announces $62M Series C to continue modernizing legal discovery   By Ron Miller@ron_miller in TechCrunch

Everlaw is bringing modern data management, visualization and machine learning to eDiscovery, the process in which legal entities review large amounts of evidence to build a case. Today, the company announced a $62 million Series C investment.

CapitalG  (Alphabet’s growth equity investment fund) and Menlo Ventures led the round. Existing investors Andreessen Horowitz and K9 Ventures also participated. The startup has now raised $96 million, according to the company.

Everlaw  co-founder and CEO AJ Shankar says eDiscovery, which has been around for years, has become a classic big data problem. “We help legal professionals sift through huge volumes of evidence in lawsuits and investigations to find the smoking gun and the incriminating email,” Shankar told TechCrunch.

The software also helps teams of legal professionals work together and collaborate around this evidence. “Turns out that the law is incredibly collaborative, and we help these teams create a work product, and communicate and collaborate with each other in a system specifically built for the practice of law,” he explained.

He says this coordination is often done manually in spreadsheets with communication taking place via email, and even companies using legacy eDiscovery software are using systems designed in a time of lower data volumes.

The company offers a variety of tools to help humans locate the information they need to build a case. There is a search feature, of course, and data visualization tools including a timeline tool that helps pinpoint when key events happened. This can help lawyers direct researchers to find evidence within that critical period, greatly narrowing the focus of the search.

And the company also offers both supervised and unsupervised machine learning algorithms to help the team find specific bits of information. He acknowledges it will take human ingenuity working with the tool to find what you need. “No one’s handing you anything on a silver platter. It absolutely requires some detective work. It’s iterative and we have tools to help you with that,” he said.

Shankar stumbled into this area of technology when he was still a graduate student in computer science and a law firm came to his department looking for a technical expert. He ended up working with the firm for a couple of years, saw the kinds of technical challenges it faced, and decided to build some tooling to help..... "

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