Frontline Solvers

Software DevelopmentNevada, United States11-50 Employees

Frontline Solvers is a leading vendor of predictive and prescriptive analytics software, used to make analytics-powered decisions. Its tools for decisions using mathematical optimization, business rules, Monte Carlo simulation and risk analysis, and data mining, text mining and forecasting are used by over 9,900 organizations -- on desktops, servers, and in the cloud. Frontline's customers can build decision models using Microsoft Excel (desktop and cloud) with its Analytic Solver® product family; in C++, C#, Java, R and Python using its Solver SDK® and XLMiner® SDK; and in JavaScript web and mobile apps using its RASON® Decision Services platform and REST API.

Over 1.2 billion copies of Frontline's Solvers for optimization have been distributed to users, in every copy of Microsoft Office sold since 1990, and Frontline's Excel Solver upgrade products, sold directly to advanced users, are by far the market leaders in their field. In 2010 Frontline won the INFORMS Impact Prize for this work. In cloud-based analytics, Frontline has over 1.5 million users of its free add-ins for Excel and Google Sheets. Frontline Solvers are also the #1 choice in MBA programs around the world, used by over 500,000 students, and covered in scores of textbooks.

Frontline's corporate customers -- including over 1,100 firms with $1 billion revenue or more -- are global leaders in many industries including financial services, aerospace, automotive, telecommunications, utilities, transportation, and manufacturing, as well as commercial and government research centers. Frontline’s line-of-business users build data mining, business rule, simulation- and optimization-based decision models, significantly faster and with less effort than with other software. These models are used to analyze risk and decide how to best allocate scarce resources - money, equipment, and people - often yielding million-dollar savings. And RASON meets IT’s goals for management and governance of analytic models.

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