Transforming legacy applications in Azure for insurtech

Project overview

A leading insurtech provider of data-driven cost management and customer support solutions for the property and casualty insurance industry looked to our team for help with a cloud transformation. The client’s legacy systems were difficult to maintain, not cost-effective, and vulnerable to security compromises.

We moved manually deployed legacy applications from an on-premise data center environment to Azure. The new applications were built as scalable real-time engines designed to asynchronously process customer tests using horizontally-scaling workers. We also revamped their mobile application.

Project goals

Virtualize the existing infrastructure, creating virtual machines (VMs) in the cloud that mimic the on-premises hardware, including the operating system, applications, and data.
Connect virtual machines to the network to facilitate communicate with the on-premises infrastructure.
Migrate storage systems used in the on-premises data center to the cloud
Establish security measures in the cloud to protect infrastructure, including firewalls, intrusion detection systems (IDS), and identity and access management (IAM)
Set up backup and replication measures in the cloud to ensure business continuity in the event of a disaster
Monitor performance and health of the infrastructure in the cloud
Thoroughly test and validate virtualized infrastructure in the cloud to ensure appropriate functioning and user accessibility
Launch cutover from the on-premises data center to the cloud

Team structure

Mobile Engineer

Backend Engineer

QA Automation

DevOps Engineer

Solutions Architect

Database Engineer

Project Managers

Tech stack

The result

The Blankfactor team rebuilt the entire infrastructure in Azure using Terraform. We also dockerized all core services and built CI/CD pipelines for governance and deployment using Azure DevOps, Kubernetes, and Argo CD.

As a result, our team helped the client modernize its applications and establish a highly performant cloud environment. The new system is optimized for cost and efficiency, scaling computing resources based on demand.

Case studies