Because of the relentless, double-digit growth of enterprise data, multiterabyte document sets are now routine as part of e-discovery. Finding requested electronically stored information (ESI) is getting more difficult, and the associated discovery costs are exploding. Multiple e-discovery vendors offer products featuring “predictive coding” for streamlining large-scale reviews, yet corporate legal teams are seeking proactive intelligence that can help them identify key documents, negotiate favorable case settlements, prepare for FRCP 26(f) conferences and reduce e-discovery costs much earlier in the process.
Fusion Predictive Intelligence™ delivers advanced machine intelligence and arms counsel with the strategic power needed to decisively change the course of discovery early in a matter. Exterro’s predictive technology capability automates the identification and classification of ESI across the EDRM. With Fusion Predictive Intelligence™, legal teams can accurately and intelligently narrow the ESI funnel early, eliminate manual processes and reduce costs.
- Apply predictive technologies and formulate case strategy in advance of collection during ECA, at the point of collection and throughout review
- Leverage all analytical tools for managing variable case needs, including keyword, conceptual, metadata and machine learning
- Make more informed decisions early in the matter by arming your legal team with better intelligence for FRCP 26(f) meet & confers and try-or-settle negotiations
- Apply predictive technologies to second requests, internal investigations, Rule 45 subpoenas, and for protection of IP and confidential information
- Rapid ESI Classification – Apply trained predictive models to data sets of any size
- Spot Prediction Analysis – Gain insights beyond what human review can by applying predictive models to individual documents and e-mails
- Defensibility Report – Illustrate defensibility throughout every step of the process, including who was responsible and how tasks were performed
- Statistical and Judgment Sampling – Specify all variables of the statistical sample as well as choose your own sample documents for effective and rapid training of the model
- Predictive Model Library – Store and re-use trained predictive models on subsequent ESI data sets for immediate application whenever new matters arise.