Case StudiesAnalytics Subsystem for a Sales Engagement Platform

Analytics Subsystem for a Sales Engagement Platform

We helped our partner to improve and power up their analytics subsystems. This system leverages the latest technologies and can work with large datasets to provide actionable insights that can accelerate sales growth.


Digital Transformation


Back End Development, Big Data & Analytics, Cloud / DevOps, Front End Development, QA as a Service, UI/UX Design


California, US



  • Complex Data Management 

Managing and processing large volumes of data presented a major challenge in developing the new analytics system. The sales engagement platform already generated terabytes of data on a daily basis from millions of customer interactions. This raw data held invaluable insights, but turning it into actionable intelligence required robust data infrastructure.

  • Integration With Existing Systems 

When bringing in a new system like advanced analytics, planning the integration to minimize disruption is critical. Users should be able to keep working in a familiar environment. Careful integration enables the new capabilities to be added behind the scenes without interfering with day-to-day workflows.


Technologies Used

The analytics subsystem leverages cutting-edge technologies and software development best practices to provide a high-performance, scalable, and reliable system. The technology stack includes Java 7, Java 8, and Java 17, as well as C# for backend development.


About the team

The team size varied depending on the stage of the product development:

Team composition

  • PM/ PO


  • Scrum Master


  • Back-End Developers


  • Front-End Engineer


  • QA Engineers




The implementation of the new analytics system had a significant positive impact for the partner company. First and foremost, the system provided a bug-free performance. By thoroughly auditing and redesigning the architecture, the developers were able to eliminate bugs that had previously caused issues. This led to higher system stability and reliability.

Additionally, the solution works significantly faster than before. The partner company benefited from a much higher level of performance by transitioning to Amazon Redshift and implementing an asynchronous analytics processing system. 

In our pursuit of efficient analytics processing, we adopted the Command Query Responsibility Segregation (CQRS) pattern as the foundation of our approach. Leveraging this pattern, we designed and implemented an asynchronous analytics processing system.

These achievements are not just milestones; they are transformational elements that have redefined the capabilities of our partner’s Sales Engagement Platform. By demonstrating our ability to manage data and process analytics efficiently and seamlessly, we have positioned our partners for success, enabling them to offer enhanced services, attract more users, and stay ahead of the competition. These accomplishments stand as powerful selling points, showcasing the tangible value we bring to our clients through our development services.

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