Customer is an American nonprofit organization that operates radio networks broadcasting contemporary Christian music through a network of more than 415 radio stations across United States. Customer operations are also supported by multiple mobile and web-based applications


  • Customer has a web application for tracking, auditing and praying user prayers. With increased load of prayer requests the manual auditing process to remove personal identifiable information was putting the prayer in audit queue for upto 6-7 days.
  • Application lacked features which would lead to a better user involvement
  • Slow feature release process leading to month lead time for new features to reach users
  • Manual translation of prayers entered in non-English language


  • Enwidth provided Machine Learning based audit module to remove last names, addresses and other personal information from the user submitted prayers.
  • Proposed and implemented new features related to prayer grouping, aging, classification and fulfilment.
  • Integrated google translation services in a way to have least cost impact
  • Optimization and automation of the release process


  • The ML based audit and language translation resulted in bringing down the audit queue from 6-7 days to under 1 day
  • Regular weekly releases with enhanced and new features
  • 2x increase in user involvement towards fulfilment of prayer requests due to better UX and new features.


Dot Net MVC, Spacy, SQL Server, Python, Google API, Azure DevOps