About the customer

  • Our customer has been one of the leading health care service providers in the Canadian market. They did had a SAAS based product which was being used by all the leading medical facilities throughout the country. They have been facing issues such as too much loading time, Search not fetching results and application providing frequent time out errors were quite common. Client wanted us to analyze and figure out the root cause of these issues.
Petro Canada




Kotlin, Swift, J2EE, ELK, Azure, Microservices

Project Duration

1 Month


Project Details

  • During this engagement our prime responsibility was to perform a code review cycle for the existing Backend, and Mobile applications (Both iOS and Android)


  • Major challenge being faced by the Client was the repeated escalation from the consumers, there have been instances where the escalation rates were hovering around to be 40 unique escalations / business day
  • No modern infrastructure setup, Client did had an issue with this as it was a SAAS based product and basic infra practises such as disaster recovery, auto up/down scaling, CI/CD wasn’t in place
  • Load time was one of the biggest issue that client was facing, current load time of the application was sometimes around more than 3 mins resulting into frequent time out
  • Lot of broken flows, This was the other problem which the client was facing throughout the application
  • There were no common modules designed for the application, Modular fashion wasn’t being followed which was leading to re-writing of the same code at many a places a daunting task for the developers
  • There were more than 5 repos that weren’t being reviewed by any lead or developer which was a challenge of the quality of the code

Our Approach

  • A code review sprint was launched, This review cycle was for 10 business days
  • We did performed a code review for the applications, all the related repos were reviewed and code quality was checked 
  • There were certain outcomes of the code review sprint which led to the following findings : 
    • No caching in place for certain frequently accessible data
    • Code wasn’t optimized and didn’t had sufficient comments.
    • No linting tool was in place, No checks were running before the code was being published
    • No reusability of the code was seen
  • For searching no ELK stack or similar practises was in place, search results were getting returned from the DB tables directly
  • There were more than 250 feedback points per repo
  • Infrastructure was the major issue, as there was no edge security in place, Neither it was covering the basic infra setup such as disaster recovery and Ci/Cd
  • ALong with the issues solutions were provided as well, how to implement the changes that were needed
  • Bigoh was awarded a project which was responsible to make these changes in the existing code base


  • Post the changes were performed which resulted into 3 unique escalations / day, that too only of the functional tasks
  • Load time was significantly improve due to implementation of ELK stack and necessary caching for frequently accessible data
  • All the components were getting loaded in the background multithreading was being used which eventually resulted in a lower loading time 
  • All the best infra practises such as ci/cd, disaster recovery was kept in place to avoid any possible future setbacks on infrastructure end