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Relativity's assisted review tools help you categorize your documents and automate the review process while minimizing the time your review team would otherwise spend coding irrelevant documents in your document set.
Review Center gives you the option of using customizable saved searches to serve up documents to reviewers, or using AI-powered queues that predict which documents are most relevant.
Sample Review Center scenario:
You’re a litigation support specialist at a Relativity service provider, and the legal department of a large financial services company reaches out to you. The federal government is demanding that documents belonging to three key custodians be turned over quickly as part of an ongoing investigation.
This company is in a serious time crunch because the government agency’s attorneys have unexpectedly requested documents from a fourth custodian, whom they believe is crucial to the case. This doubles the size of the data they’re required to review and produce, so they turn to you. You turn to Review Center.
You create a saved search that includes the data of all four custodians, then use that as the basis for a prioritized review queue. The queue includes documents that were previously coded as relevant or non-relevant. Review Center’s AI classifier analyzes the previous coding decisions, then serves up documents to reviewers based on its relevance predictions.
As the review continues, the AI classifier grows better and better at predicting which documents are relevant. In the end, you find that reviewing less than 15% of the total documents still produced accurate results. The financial services company you’re assisting can now easily comply with the federal government and give them what they need, despite the limited time frame.
Note: The Active Learning application is now read-only. Create new projects in Review Center.
If you have Active Learning and Review Center installed in your workspace, you can view data for previous projects on the Active Learning History tab. Please note that uninstalling Active Learning removes the project data.
For more information, see:
Review Center
Active Learning
- Active Learning application history (accessed through Review Center)
- Active Learning
- Creating an Active Learning project
- Choosing an Active Learning review queue
- Best practices for Active Learning review
- Monitoring an Active Learning project
- Project Validation and Elusion Testing
- Reusing trained models
- Review statistics
- Active Learning performance baselines
On-demand training
- Getting Started with Review Center
- Active Learning: No Batches Required
- Project Validation Strategies in Active Learning
- Getting the Most out of Active Learning: Workflows and Use Cases
- Active Learning Ask the Expert: Expanding your Options with Family-Based Review
- Your Active Learning Questions Answered
