RelativityOne’s secure cloud platform gives you the tools you need to tackle your unique challenges through every phase of a project.

Secure e-Discovery
RelativityOne is designed to be a trusted and reliable e-discovery solution.

Investigative Approach
Use advanced analytics, machine learning, and visualisations to quickly sift through volumes of unstructured data and uncover the facts.

People-Centric Review
Get a better understanding of the people and relationships in your matter with intuitive ways to analyse communications across people and channels.

Comprehensive Platform
Gain access to all the tools you need at each step of the e-discovery process to ensure you can tackle even the most complicated cases.

Collect from Popular Cloud Technologies
Collect directly from the most popular enterprise technologies without ever leaving the cloud – so your data remains as secure as possible.

Flexible Data Management
Take full control of your data–from managing a repository for Early Case Assessment (ECA) or investigations to leveraging longer-term options like RelativityOne Store and cold storage for inactive matters or collection data.

Cloud Elasticity
Keep up with performance during peak times, without the work or expense of buying, installing, and maintaining your own infrastructure.

Assisted Review (Continuous Active Learning or CAL)

Assisted Review amplifies your e-discovery efforts with sophisticated machine learning technology. It uses a powerful analytics engine to find relevant documents faster with full transparency, backed by defensible statistics. Utilise either the Active Learning or Sample-Based Learning workflows.

Active Learning

RelativityOne’s Active Learning workflow continuously learns what’s important to your matter to quickly get to the heart of it. As you code documents, RelativityOne will keep a pulse on coding decisions in real time, constantly refining its understanding of what’s responsive to get smarter as the review progresses. Simply code documents and the most relevant ones will be served up next, so reviewers can always take advantage of the system’s most current understanding.

Sample-Based Learning
RelativityOne’s sample-based learning workflow uses sampling to slice a document set in various ways, making sure you get coverage across all of your documents as you’re
training the system to make decisions. Using a seed set of human-coded documents to train the system, coding decisions are suggested for the remaining document universe, so you can immediately begin quality control to refine the system’s understanding. Easily sort your documents into groups, so you can have responsive documents reviewed by the most qualified experts and uncategorised not responsive ones passed to other reviewers.


Email threading allows you to quickly and easily understand the structure of an email thread and identify missing information. You also can select entire conversations to make quicker and more accurate coding decisions.

Identify documents that are nearly identical, such as multiple versions or drafts of the same document, so you can review them together for a more efficient review.

Keyword expansion allows you to discover unexpected or hidden words, such as project code names and company or industry jargon. Concept searching finds information without an exact word or phrase.

With your documents categorised, you can prioritise data for review, find important documents from an opposing production, and ensure quality control by automatically identifying and coding documents similar to those you’ve already tagged.

Conceptually visualise your data to narrow in on what’s important, even with the most complex projects.

Explore communication networks across your matter to dig deeper into conversations. Learn who’s talking to
whom, how often, and what about.

Our Relativity Certifications