Zementis, Inc. provides software solutions for predictive analytics.
The company was founded on the principle that data science teams and IT departments can collaborate seamlessly and efficiently, allowing predictive models to rapidly move from development to deployment, so that businesses and other data-centric organizations can easily incorporate predictive analytics into their routine operations. Agile deployment of predictive solutions is the cornerstone of the Zementis philosophy.
Core solutions include ADAPA®, a decision engine for predictive analytics, and UPPI™, a universal plug-in utility for industry-leading analytics and data warehouse platforms. Zementis customers can deploy these solutions on-premise or on the cloud, with access via an intuitive Web-based console, via one of multiple industry-leading analytics platforms or as a simplified Hadoop interface.
Zementis solutions benefit data-centric organizations by:
- Accelerating intelligent decision making via predictive models
- Reducing cost and complexity of predictive analytics
- Enhancing adoption and encouraging utilization of predictive analytics across multiple business functions and end users
- Ensuring scalability and reliability of model execution via a uniform deployment platform
- Supporting highly dynamic and complex data environments
Zementis customers include leading financial services institutions, marketing and advertising agencies, consumer and enterprise technology solution providers, telecommunication service providers and government agencies. Customers such as FICO, the United States Army and Verizon have used Zementis solutions successfully to enhance their predictive analytics capacity and capabilities. Zementis partners with leading analytics and data warehouse solution providers to enrich and extend customer capabilities. Supported partner solutions include: IBM PureData (Netezza), Pivotal Greenplum, Teradata, Aster, SAP Sybase IQ and Datameer.
The company was founded in 2004 and has offices in San Diego, San Francisco and Hong Kong.