Prioritize Documents and Dramatically Improve Review Quality and Speed with CategoriX
In any matter, the ability to find critical documents can make or break a case--but the sheer volumes of data in large-scale document review can result in a trade-off between review speed and accuracy.
With CategoriX, a technology-assisted review solution developed by scientists at Xerox Research Centre Europe, there is no trade-off. CategoriX iteratively learns from attorneys most knowledgeable about the matter, and applies statistical and machine-learning techniques to prioritize, or rank, documents based on how likely they are to be relevant.
CategoriX is offered as a service, with no hardware or software to purchase, install or maintain. Our technical, search and statistical experts guide the technology and workflow, with a team lead serving as primary contact for all support and project details.
With CategoriX, legal teams can:
- Accelerate review for a range of review tasks – document prioritization, QC enhancement, first-pass review, issue coding and defensible document reduction
- Zoom in on the key documents that merit attention, leading to earlier case insights and a faster path to production
- De-prioritize documents classified as non-relevant, saving time and reducing expenses
- Achieve greater accuracy throughout the review with consistent application of expert assessments
- Enhance review quality with built-in algorithms that flag coding discrepancies
- Optimize workflow by seamlessly integrating CategoriX-ranked documents into reviewers' assignments on the OmniX™ review platform
- Rely on an iterative process and measurable results that stand up in court
How Does CategoriX Work?
CategoriX relies on the assessments made by our customers' attorneys most knowledgeable about the matter. In a CategoriX review:
- Legal experts provided by customer and subject matter experts assess samples of documents from the review population
- CategoriX models are built based on these coded documents
- CategoriX iteratively incorporates feedback from coded and QC’d samples to progressively improve the accuracy of its relevance scoring
- Once optimal accuracy is achieved, CategoriX reviews the rest of the document population and ranks each document according to how likely it is to be relevant