What's Under The Hood

Massive Scalability Allows for Intelligent Management of Today's Data Stores.

Servient is built on a true “Big Data” platform. The building blocks of Servient include Hadoop, HBase, SOLR and other technologies that power the largest web companies in the world. Servient breaks down large volumes of unstructured data into small computation tasks that can be executed in parallel. Servient then distributes the computation across multiple resources allowing the application to perform at scale.

Servient permits companies to quickly ingest data into the Servient archive and automatically organize the data using Servient's advanced machine-learning models. Servient allows the enterprise to search, explore and analyze their unstructured data content at scale.


Solr is a scalable enterprise search engine that Servient uses to drive its powerful search function. Servient’s machine learning technology combined with Solr’s major features that includes powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration and rich document (e.g., Word, PDF) handling greatly improves the rate of recall.


Spark enables Servient’s machine learning to run at enterprise scale. Servient provides a variety of proprietary supervised and unsupervised machine learning algorithms that work in unison with those available on Spark. This blend of Servient’s proprietary algorithms working alongside Spark’s algorithms enables sophisticated workflows coupled with search to identify and categorize the relevant information in a highly superior manner than our competitors.

Web Scale Processing With Modern Data Architecture

Servient delivers modern data architecture that combines web scale processing with archiving technology. It connects, archives and processes a variety of unstructured and structured enterprise data sources. Servient's NoSQL data model allows support for multiple extensible evolving taxonomies. The solution enables several sophisticated workflows that combine search with unsupervised and supervised machine learning algorithms running on the platform allowing the user to visualize and interact with the data in order to search and categorize. The end user has seamless integration to utilize Servient’s compliance and eDiscovery applications to interact with the data repository

Real World Web-Scale Application

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Servient helped 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 eDiscovery Platform's web scale capability was utilized to help the client create an efficient process that saved $2 million.

Download The Case Study