Everlaw Predictive Coding
Prioritize your most critical documents with AI-powered review.
Prioritize Critical Evidence
Predictive Coding learns from your team’s own coding decisions, helping you frontload high-value evidence, and offering greater command of your case. Jump-start new matters using previously trained models across your organization, and overlay these predictions onto visual clusters so you can get a better view of your entire corpus.
Powerful Technology-Assisted Review
Everlaw’s Predictive Coding scales with your team by learning from existing review decisions to predict how your team will evaluate the remaining, unreviewed documents. Confidently move through massive document sets and identify your most valuable evidence first.
Turn High-Volume Discovery into a Strategic Advantage
Immediately identify and de-prioritize non-responsive files to focus your review energy on the most critical evidence. Predictive Coding turns data sets into structured evidence, giving your team complete command of the key facts early in the matter.
Never Start from Scratch Again
Save your models to use across matters, jump-starting projects with your previously-trained Predictive Coding models. By continuously refining its understanding of relevance, the model ensures document rankings are grounded in your team’s precise legal judgment rather than static rules.
Multi-Matter Models
Create powerful libraries of previously trained predictive coding models, available for use in all projects across your organization. By iterating on this institutional knowledge, you’re able to gain a more comprehensive command of the facts, insights, and strategy of your case.
Audit-Ready Model Validation
Verify model performance in real time with built-in recall and precision metrics. Everlaw provides clear visual statistics and control sets that prove model performance, ensuring your document prioritization strategy holds up under judicial scrutiny.
Overlay with Clustering
Map your Predictive Coding results on top of Everlaw’s Clustering visualization for an interactive heat map of your entire corpus so you can find documents faster.
Getting through huge data sets is already daunting. Everlaw not only provides these powerful tools – specifically Predictive Coding and Clustering – it provides the training and support necessary to utilize them efficiently.
Leah Thompson
Litigation Paralegal, Bienert Katzman Littrell Williams LLP
Frequently Asked Questions
How does predictive coding help legal teams review documents more efficiently?
Predictive coding uses machine learning to identify patterns in reviewed documents and prioritize other documents that are likely to be relevant. This helps legal teams focus review efforts on the most important materials first and reduce the amount of manual document review required.
How does Everlaw Predictive Coding support Technology-Assisted Review (TAR)?
Everlaw Predictive Coding applies TAR techniques to help legal teams train models based on reviewer decisions. As reviewers code documents, the system continuously refines its predictions to improve prioritization and streamline document review.
How can predictive coding improve large-scale document review projects?
Predictive coding improves large-scale document review by helping teams prioritize likely relevant documents first, so reviewers spend less time on low-value material and more time on what matters.
How does Everlaw ensure transparency in predictive coding workflows?
Everlaw supports transparency in predictive coding workflows by making the model’s inputs, outputs, and validation metrics visible to users rather than treating the workflow as an opaque ranking engine. This helps legal teams maintain control over the review process while using machine learning to improve efficiency.