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This case study describes how Pragmatic Works implemented Microsoft Azure SQL Database (v12) to help

This case study describes how Pragmatic Works architected a scale-out solution that essentially doub

This case study describes how Pragmatic Works saved an oil and gas company in both man hours and res

This case study describes how Pragmatic Works used Microsoft "R" and the Azure Cloud to help the ana

This case study describes how Pragmatic Works used Predictive Analytics and Azure Machine Learning t

This case study describes how Pragmatic Works used Predictive Analytics and Azure Machine Learning t

This case study describes how MetaBank's Meta Payment Systems (MPS) division leveraged Workbench to

This case study describes how The Energy Authority leveraged LegiTest's automated data testing to im

In this case study, you'll learn how Pragmatic Works' Task Factory helped The University of Calgary

In this case study, you'll learn how Pragmatic Works doubled a large human resources consulting firm

In this case study, you'll learn how we assisted a large, non-profit health agency in meeting their ...

In this case study, you'll learn how a large manufacturing company saw dramatic performance improvem

In this case study, you'll learn how Mobile Medsoft increased productivity and performance through a...

Using Microsoft Azure SQL Database (V11) to host their backend database, Mobile MedSoft’s application is multi-tenant and used by their clients 24/7. It has been through several performance tuning projects over the lifetime of the database.

The client's application utilizes SSAS and SSRS components of the Microsoft BI platform. The goal is to aggregate data from all employees, then analyze the data based on organization hierarchy and other employment data points.

An independent oil and gas company focused on the acquisition, exploitation, development and production of oil and gas properties was struggling with platform architecture for their growing data volumes.

The analytic and actuarial department of a large insurance company had a need to grow and expand their current data analysis. The existing environment was rigid and did not support complex code development.

A large insurance provider was looking to identify trends in their data to make better business decisions with several lines of business, including Rental Policies and Extended Warranty Policies.

A large academic testing company that operates centers for standardized testing needed to identify patterns in their data to help expose fraud and cheating. Manual processes were not satisfying the business requirements and a predictive machine learning process was needed.