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Product Information

Complete E-Discovery Solution

Complete E-Discovery Solution.

The Most Extensive Set of Features on The Market. Scales for Matters of Any Size.


Complete Solution

Unstructured Data Archive

Built on Hadoop Providing Unstructured Data Management at Scale.



Servient Reduces Costs by up to 75%

Servient Reduces Costs by up to 75%

Reduce the review of irrelevant data with Servient.

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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.

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White papers


Practical Uses of Active Learning Technology to Improve the Quality of E-Discovery and Control Costs.

The ever increasing volume of data that lawyers must handle in litigation requires the legal profession to evolve beyond the current e-discovery process. The process of applying a simple keyword filter followed by linear review simply does not to meet the challenge of today's e-discovery.

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Case-Centric Search and Sampling Protocols. The Key to Defensible Search and E-Discovery Cost Containment.

Use of a defensible search and sampling protocol has emerged as the number one way to control the cost of e-discovery. An iterative search process that allows for continual query refinement and repeated sampling achieves the best results and delivers the most effective cost containment. This White Paper presents a strategy we refer to as a "Case-Centric" protocol which follows a tried and true legal analysis approach to the design of appropriate searches and validation of the results through statistical sampling.


Case Studies


Servient helps guide a client over a mountain of data.

In response to requests from federal regulators, a global corporation was challenged with finding responsive documents within 30 terabytes of data. This case study outlines how Servient's E-Discovery Platform was utilized to help the client create an efficient process that saved $2 million.


Case Study

Servient helps an energy company reduce review costs in a regulatory investigation with machine learning.

A large energy company was required to respond to a Federal Regulatory subpoena.

The company faced the need to review a large volume of documents in a short period of time to meet a deadline. The company wanted to fully comply with the subpoena and control its costs.