Client background and business context

  • Client profile: Europe’s No 1 car dealership group with €11 billion in revenue (2017).
  • Project: Create a centralised stock and pricing platform.


Being Europe’s biggest automotive retailer, the company had an extensive network of over 1,000 dealers (partnered or owned dealers). With such a network, having relevant insights regarding prices and stocks is a key aspect of increasing efficiency and, consequently, revenue.

To achieve this, a centralised platform was needed that would respond to the following objectives:

  • Offering data insights: Gathering data from an extended network of over 700 partnered dealers and hundreds of owned dealers to a central point that the company’s business users could easily access.
  • Enabling data enrichment: Upgrading existing data with information coming from external partner platforms.
  • Implementing a pricing engine: Creating an AI/ML engine that could recommend vehicle pricing.
  • Respecting time to market: The platform had to be able to adopt a high-speed release cycle.
  • Offering a consistent experience throughout the company’s ecosystem: The platform had to be cross-platform-ready regardless of the touch points used (desktop, web, IOS and Android tablets).
  • Improving efficiency: Reduce to a minimum manual data entry.

Our approach

To better respond to the challenges and business objectives, we initiated a two-day discovery workshop with the company’s stakeholders. As part of our initial efforts, the discovery process enabled us to quickly determine the needs, goals, and success criteria of the future platform.

After the discovery period, we assigned an autonomous product team that, together with the stakeholders, attempted to find the most suitable solution to address the company’s challenges. Our team covered all areas, from architecture, data, and business analysis to UX design and back-end implementation.

The chosen architecture is based on standard Google Cloud solutions and took an API-first approach so that other applications could benefit from the platform, centralised data, and the AI pricing engine.

To support the goal of fast delivery, we proposed a Kanban approach to ensure constant high-speed delivery and regular client interaction.

We put in place a CI/CD pipeline for more efficient testing and to easily deploy the code to various environments like integration, staging, or production.

Technologies used in this case study


  • Spring by Pivotal
  • Google Cloud
  • NodeJs
  • Java
  • GitLab
  • JHipster
  • React
  • Beam
  • Swagger
  • Maven
  • Git

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