Our client, a leading global financial services provider, sought to enhance the efficiency of their developers and tools. They encountered significant challenges with C++ integration within a Bazel monorepo environment.
Our client aimed to optimize the development of their internal projects by centralizing their deployment platform. Doing so would speed up the development and free up manhours of their in-house engineering team.
A top-tier financial institution aimed to deliver a large Scala project. Due to the size and complexity of the undertaking, the tooling that the client used was underperforming, causing delays in the delivery.
A global retailer offers over 200 domain-specific services for their business operations, providing essential information on both physical and virtual store locations worldwide to 170 internal departments via an enterprise API.
VirtusLab helped design and migrate many systems, resulting in faster delivery and better quality in the cloud infrastructure area.
Our client, a flexographic printing manufacturer, specializes in producing labels and soft packaging. Their customers demand an accurate and quick estimation of production costs and lead time.
Our retail client collects and processes data from their online store on-premises. Their growth in customer data posed a potential threat to business growth since the remaining solution showed processing limitations.
Our client, a worldwide operating retailer, was spending £350M per year on the fulfillment and delivery of groceries solely in the UK.
Our client, a global retailer, aimed to enhance the security of their system since hackers carried out fraudulent activities on their E-commerce site.
Our client aimed to optimize their internal tools. The existing system lacked warehouse management functionality, which created discrepancies between the amount of raw materials on-site and in the registry.
In today's competitive retail landscape, forecasting demand accurately makes or breaks any business. A major global retailer relied on a slow and resource-intensive forecasting process that made it hard to keep up competitiveness.
Our client, a global retailer, aimed to automate their Machine Learning (ML) pipeline to leverage user behavior data and gain a competitive edge by predicting customer needs more accurately. To do so, the retailer lacked the expertise in Big Data, software development, and data transformation.
