If there is one thing common to all legal cases, it is documents. In decades past, the evidence collected in litigation was often confined to digging through folders and filing cabinets, in a process called discovery. Today, electronic discovery, or ‘ediscovery,’ is the name of the game — with paper documents replaced by millions of emails, Slack messages and Zoom calls.
MarketsandMarkets estimates the global ediscovery market size to grow from $9.3 billion in 2020 to $12.9 billion by 2025. Driving that growth is a focus on proactive governance with data analytics; the emergence of new content sources; an increase in the amount of litigation across the globe; and an increase in electronically stored and social media penetration.
For several years, AI has helped modern law firms deal with the first amount of data gathered during the discovery process, increasing the potential available evidence. Today, Everlawthe cloud-native investigation and litigation platform, unveiled its Clustering Software feature, delivering what it says is a “breakthrough” in terms of its scale, visualization, ease of use and ability to conduct true discovery – that is, the ability to discover new evidence that helps build compelling storylines.
AI-driven exploration a must for modern law firms
This explosion of digital communication means lawyers are working with more and new kinds of, data than ever before. Understanding and interpreting this data can be overwhelming, time-consuming and costly. With ediscovery tools, legal teams can find key evidence by scanning thousands of documents and files in a matter of minutes to quickly identify relevant items. Since just one page or sentence can make or break a case, the ability to group similar pieces of evidence together can be a game-changer when uncovering a needle in a haystack.
To tackle those challenges, Everlaw uses an unsupervised machine learning system to cluster together documents by conceptual similarity and generate insights without requiring any user input. “Think of it as a map for the haystack,” said AJ Shankar, founder and CEO of Everlaw.
Everlaw decided to address the clustering challenge because while Technology Assisted Review (TAR) has been allowed for about a decadethe company maintains that the promise of clustering has fallen short – it says other tools are difficult to use or can’t scale to meet today’s video, audio and text demands.
What differentiates Everlaw from its competitors including Relativity, Exterro and KLDiscovery? Shankar argues that Everlaw has taken a novel approach to clustering with its hierarchical design.
“Many legal tech companies display their data as a wheel, which is limited in function. Everlaw’s clustering AI has a map-like display, representing documents spatially, preserving similarity relationships,” he explained.
This visual format encompasses both a 30,000-foot snapshot and a granular, down-to-the-document view. The goal is to provide legal teams with a baseline understanding of the document set without needing advanced setup or extensive technical expertise. It is designed to pinpoint more specific and relevant information than other AI tools or keyword searches and quickly identify which documents need human review, reducing the risk of errors in investigationy.
“Legal teams can simplify scope negotiations by helping both sides identify and agree on which materials are actually relevant and require review” Shankar explains. “They can even use clustering to prioritize documents sets for review to ensure that subject-matter experts are looking at documents relevant to their area, or that senior review teams are spending their time on the trickiest documents to review.”
The future of AI and discovery
The haystacks of evidence are only going to get larger as digital communication continues to flourish, especially with the new paradigms of hybrid and remote work. And there’s no doubt that AI will be vital in helping legal professionals deal with this exponential growth in data, since their budgets and headcounts will not be growing concurrently.
AI tools in ediscovery, Shankar added, can now help legal teams sort through and understand millions of documents, versus thousands historically. According to Everlaw, more AI-powered features will continue to be developed and adopted in the ediscovery space, including automated audio/video and metadata redaction; automated recommendations in case deposition tools and communication pattern analysis.
These evolving challenges and opportunities are precisely why Shankar founded Everlaw in 2011.
“I believe that the law is an essential pillar of civil society and it deserves state-of-the-art technology,” he said.