Resolve cases faster
During his legal career, Manny identified two competing interests which have historically posed a challenge:
1) the desire to gather as much information about a case as possible in order to have a holistic view of all of the facts, and
2) the need to distill a case down to just the key facts in order to be laser focused on the information necessary to win the case.
However, it became clear that the more information gathered, the more difficult it was to focus on the key facts.
As Manny put it, there was a "serious problem with information overload." Indeed, extracting relevant facts out of pages and pages of unstructured notes, transcripts, produced documents, etc. was an arduous and time-consuming process. The desire to gather as much information as possible was at odds with the need to litigate a case efficiently and effectively. “The goal is to be the most knowledgeable person in the room” - that’s an easy path to success. The question for lawyers should be - how do you get to that point most efficiently?
Manny realized that AI, and large language models specifically, were the key to solving this problem. With the help of AI, it would be possible for an attorney to input large volumes of unstructured data and case information and get back a focused and organized case fact summaries and theories. Being an innovator, and with a background in technology, Manny attempted to tackle the problem himself by building an internal AI tool – a herculean endeavor. However, after the scope of the project became significantly larger than anticipated, he looked into the market’s offerings – which is when he found Eve.