What Makes Servient Different

Servient exists to contain legal costs. For over a decade, Servient has worked to address the ever-increasing data volumes involved in legal matters. Servient's industry experience has resulted in the development of a mature technology solution that reliably identifies and organizes relevant data with limited lawyer review. Servient combines proprietary machine learning technology and workflows with an intuitive, refined user experience. Servient deploys its advanced analytics over the Hadoop framework, allowing it to scale and easily handle matters of any size.

Predictive Review

Fully Integrated Machine Learning For E-Discovery

Servient’s Predictive Review is the practical application of machine learning technology to e-discovery. Servient combines attorney review with advanced machine learning to substantially improve the efficiency and accuracy of identifying relevant documents in litigation and regulatory investigations.

Servient delivers advanced machine learning technology that streamlines the identification of relevant documents and speeds legal review. Servient's powerful technology fundamentally changes the economics of e-discovery.
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Recent News

Servient’s E-Discovery Software Enables Greater Cost Reduction with Consensus Review Workflow For Technology Assisted Review

Friday, June 28, 2013
Servient reduces the burden of lead attorneys by leveraging their knowledge across reviewers.

Servient to Present at Duke Law Center For Judicial Studies Conference on Technology-Assisted Review

Wednesday, April 17, 2013
Servient CEO, Ian J. Wilson, Esq., will present at the Duke Law Center For Judicial Studies Conference.
Customers and Partners

"I have seen many of the products on the market and I believe Servient offers some of the best, most effective tools. Their platform makes it easy to implement and take advantage of their powerful machine-learning technology" Brian E. Calla, Member, Eckert Seamans

A Deep Dive Into Predictive Review

A Deep Dive Into Predictive Review

Explore the process and technology required for defensible review in an adversarial setting.