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.
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.
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.
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.
Servient’s proprietary machine learning is mature and proven in the market. Servient is based on an active, ensemble approach to machine learning. Servient continuously refines and improves itself by actively selecting training documents for review.
Servient automatically prepares the data for the machine-learning e-discovery workflow upon ingestion.