For years, Relativity Assisted Review has amplified review teams’ efforts, saving millions of dollars in the process. It’s been a crucial component of Relativity’s customers’ workflows, and they’ve always shared their ideas for how Relativity can make it even more valuable. We’ve delivered on their input with something big: active learning.
Active learning puts the most relevant documents in your reviewers’ hands—fast. It does this by continually learning, in real time, from your team’s coding decisions and using those decisions to deliver the documents that matter most. It’s a recent addition to the Assisted Review workflow and will revolutionise how you run your review. Here’s how.
1. You’ll get to the good stuff faster.
Active learning keeps a pulse on coding decisions in real time to refine its understanding of what’s responsive. As your project progresses and reviewers code more documents, the engine gets smarter, analysing the coding decisions and constantly refining its understanding of what’s most important to your matter—so you can get to the heart of the issue faster.
2. You’ll spend less time on setup and administration.
Getting to the most important documents doesn’t have to take a lot of effort. Active learning handles the brunt of the work, with minimal setup and human input. You can take a 100,000-document project from setup to review in under 10 minutes. There’s no need for training sets, no manually batching documents. Reviewers simply log in, click a button and start reviewing the most relevant data. And because the review queue of documents is continuous, administrators don’t have to worry about any next steps and they can easily monitor the results.
3. You can flex your analytics muscles to meet the needs of any review project.
Active learning is the new kid on a block full of unstructured and structured analytics tools. Combine email threading, clustering, sample-based learning, and visualisations with active learning to create unique workflows that match the needs of your project—whether it’s investigating the merits of a claim, sorting your data into key issues or preparing evidence for litigation.
For example, you can use cluster visualisation to narrow in on relevant documents based on search terms and key players you’ve determined as important, then use those documents to kickstart active learning. Right off the bat, the active learning engine will have a solid understanding of what’s relevant—based on your coding of relevant documents—and deliver more of those types of documents to your team.
Ann Marie Lane is a senior manager of product marketing for text analytics and review in Relativity, working closely with the product teams to understand customer needs and strengthen the platform. She has nine years of experience marketing for the technology and software industries.
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