Enterprise Level Solution To Predict Call Volumes Using Kubernetes & GCP

Case Study

The Client

The Client is a leading custom Business Process Management (BPM) service provider. They specialise in assisting customers to stay ahead of the curve by providing transformational solutions that re-imagine business processes and deliver increased efficiency, deeper insights, and superior results.
With offices in the United States, the United Kingdom, the Philippines, and India, they are trusted custodians and long-term partners to more than 100 leading brands.

Project Objective

The Client wanted to use Analytics to bring additional insights to their clients by leveraging data collected across various business verticals such as retail, BFSI, and telecom.
As their codebase grew in size, they wanted to modernise the architecture of their Analytics platform, particularly in terms of portability and scalability, which included an efficient way to deploy new features.
In addition to it, the client wanted to devise a highly scalable and completely secure method of integrating the massive amount of data coming from various sources.

As a means to cater to the requirements, Niveus proposed to collaborate with their team of data scientists to enhance the capabilities of their existing Analytics platform and make it a scalable SAAS offering, that would be secure and easily deployable.

Solution Roadmap

  • A cloud-based microservices architecture that can be applied to frequently used nodes or services
  • Containerization for easy maintenance and integration of analytics platform
  • Build a prediction system that forecasts the call volumes for the near future, based on historical data.
  • A platform with multi system integrations and provision to import various data sources is to be built using wrappers and run on Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE).

Business Solution

  • Built a multi-tenant SAAS platform to serve the client with multiple machine learning data model across industry vertices

The platform :

  • Caters to multiple customers with pick and choose data model
  • Allows for easy build and deploy pipelines
  • Runs on microservices-based architecture as a means to facilitate easy diagnostics and fix for even the smallest of bugs/breaks
  • Runs on Canary model to de-risk any releases to production & ensure 100% platform uptime
  • Scales adaptively basis computing load and customers
  • Has integrated security features such as Cloud Security Command Center, Forseti Security, and Binary Authorization.
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The Impact

Multiple prediction runs with improved accuracy, and an improvement of 20% in the prediction time
Independently scalable components for desired scalability
Faster time to market for new feature releases
A cost-efficient solution that is easy to use.
An revamped platform in terms of deployability, adaptability, ease of use and ease of integration.
Improved Agility with the decentralization of data management with microservices.
Easy integration and automatic deployment.

Technology Stack

Cloud Security Command Center
Forseti Security
Binary Authorization
TLS and SSL
Istio
Security Scanning with Owasp
Code-based Security Scanning
JSON Web Token Authentication

Customer Testimonials

“I wanted to thank you and the team for the hard work they have put in developing the AnalyticsFIRST platform. The microservices architecture and the approach of leveraging Kubernetes cluster on GCP to containerize the platform gives us the flexibility right from development to deployment. The groundwork done by the team for integrating various Analytical API’s has helped us in making it an enterprise-grade platform. It is worth noting the extra effort that the team put in to make sure that the platform passed all security tests and ready to ingest, store and work with various financial and healthcare datasets. I hope this is just the start as we embark on our marketing and sales journey for the AnalyticsFIRST platform. Thanks and Keep up the good work!!”

Ananth KumarDeputy General Manager at Firstsource

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