RE Document Review

Rational Review

RE’s Document Review solution utilizes Rational Review, our full-featured litigation repository, which provides rich capabilities for document analytics, review, and production. It includes: direct document loading via our Rational Governance agents or through Media Manager, a simple but powerful import tool; analytics, such as concept search, communications mapping, near-duplicate and thread detection; as well as coding, redaction, and export capabilities. RR can be hosted on our secure and redundant data centers or within an enterprise to help keep data secure; both options allow for immediate upload. Granular security allows for access by various constituencies to the same data store, which is especially useful in bankruptcy and multi-district litigation. RR’s cloud pricing is simple and predictable, with a fixed-fee schedule for unlimited users and matters, tiered based on the amount of data.

Rational Analytics

RE Document Review also includes Rational Analytics’ machine-learning-based predictive coding technology, the most accurate, cost-efficient, and defensible platform available today. RA uses a small, representative sample of coded documents to calculate and validate a statistical model that can be applied to code the balance of the corpus. Unlike other offerings, RA tailors its classification technique by employing different classification methods based upon the unique characteristics of the data and the task at hand. RA is not limited to coding for responsiveness and privilege – subject-matter experts can train the system for any relevant issue.

By leveraging the intuition and judgment of the firm’s smartest lawyers, our technology dramatically reduces the amount of irrelevant data, while increasing the quality of lawyering.

In addition, RA provides transparency through comprehensible output reports, displaying the unique characteristics of documents that informed coding/classification decisions. The result is improved accuracy and defensibility with massive reductions in time and cost compared to manual classification.