In healthcare industry, providing accurate and efficient treatment to patients is of utmost importance. However, doctors and clinicians often face challenges in prescribing the most appropriate medications and services for various patient concerns. This case study focuses on developing a Product Recommender application to address these challenges and enhance the prescription practices of doctors, ultimately improving patient care and increasing revenue for the organization.

The success of many organizations today relies heavily on the uptime of their critical applications. However, maintaining optimal uptime can be challenging, especially for organizations with large, complex IT environments. This case study outlines how a big data approach leveraging machine learning algorithms was used to improve the uptime of critical applications and generate significant cost savings for a customer.

iLink helped one of the top pet veterinary hospital chains in the United States to develop a predictive model for forecasting the demand for Doctor of Veterinary Medicine (DVM) needed to treat patients.

The client is a leading global engineering firm specializing in the assessment and rehabilitation of underground sewerage systems. With a vision to revolutionize the way inspections are conducted, they sought to develop a cutting-edge Computer Vision-based program that could automatically identify defects within sewer pipes. The program would allow the user to easily go through the video frames and identify defects, and their clock positions, and allows users to see and interact with the web interface.