The Decision Intelligence Tool You Can Use for Real Results

Want to learn and use data science (predictive analytics) and/or management science (prescriptive analytics)? Use our powerful SDKs from your favorite programming language, and our learning resources to get results quickly.

You can even work directly with a business analyst in your company who uses Excel. Analytic models in our RASON modeling language or in Excel run directly in our SDKs and REST API.

Want to get results from analytics in months, not years? That's what we do best. Use our powerful software tools and training to get quick wins on your first projects, while you grow analytics expertise in the people you have.

Enable business analysts and developers to work together, to quickly move a project from a prototype (easier in Excel) to production in a server or cloud based application.

Quantitative risk analysis is the practice of creating a mathematical model of a project or process that explicitly includes uncertain parameters that we cannot control, and also decision variables that we can control. Monte Carlo simulation explores thousands of possible scenarios, and calculates the impact of the uncertain parameters and the decisions we make on outcomes that we care about -- such as profit and loss, investment returns, environmental results and more. Industries where simulation and risk analysis are heavily used include:

• Pharmaceuticals: Modeling R&D and clinical trials
• Oil & Gas: Modeling drilling projects
• Insurance: Modeling frequency and types of claims

Data science software tools help users find patterns and hidden relationships in data, that can be used to predict behavior and make better business decisions. A machine learning algorithm "trained" on past observations can be used to predict the likelihood of future outcomes such as customer "churn'" or classify new transactions into categories such as "legitimate" or "suspicious". Other methods can be used uncover "clusters" of similar observations, or find associations among different items. Common applications include:

• Financial Services: Fraud detection, good vs. bad credit risks
• Direct Marketing: Segmentation to improve response rates
• Electoral Politics: Identifying "most persuadable" voters