As digital transformation accelerates, enterprise engineering teams face a dual challenge: delivering new features at breakneck speed while maintaining the structural integrity of complex legacy codebases. For AutoScout24—Europe’s largest online car marketplace with over 30 million monthly users and thousands of dealer partners—this challenge is magnified by scale.

To maintain its market leadership and empower its distributed engineering organization, AutoScout24 Group turned to OpenAI’s generative AI technologies. By integrating Codex-powered tools (such as GitHub Copilot) and ChatGPT into their daily development cycles, the company has established a blueprint for modern, AI-augmented software engineering.

Operating a pan-European marketplace requires a highly complex microservices architecture. AutoScout24’s engineering teams manage hundreds of services written in multiple programming languages, including Scala, Kotlin, Java, and TypeScript.

Before adopting AI tools, the company faced several common enterprise software bottlenecks:

  • Cognitive Load: Developers frequently had to context-switch between different codebases, libraries, and legacy systems.
  • Onboarding Friction: Bringing new engineers up to speed on proprietary architectures and domain-specific APIs took weeks of manual mentorship.
  • Testing Overhead: Writing comprehensive unit and integration tests, while critical for maintaining site reliability, consumed a significant portion of sprint cycles.

To overcome these hurdles, AutoScout24 sought a solution that could act as a force multiplier for their engineers, reducing cognitive load and allowing them to focus on high-value system design and product innovation.

AutoScout24 pioneered the adoption of OpenAI’s models across its engineering departments, focusing on three core pillars: code generation, quality assurance, and knowledge democratization.

By deploying GitHub Copilot (powered by OpenAI’s Codex model) directly into their Integrated Development Environments (IDEs), AutoScout24 gave developers a real-time pairing partner. The AI assists in writing boilerplate code, suggesting entire functions based on natural language comments, and translating code between languages.

For instance, when migrating legacy Java services to more modern Kotlin stacks, engineers used Codex to draft initial translations. This dramatically reduced the time spent on syntax adjustments, allowing developers to focus on optimizing the architecture of the new services.

Writing tests is often treated as an afterthought in fast-paced environments, leading to technical debt. AutoScout24 flipped this script by utilizing ChatGPT and Codex to automate the generation of unit tests.

Engineers can feed a block of code into the AI and request a comprehensive suite of edge-case tests. This has not only increased test coverage across their microservices but has also helped identify hidden bugs before the code ever reaches the continuous integration (CI) pipeline.

To streamline knowledge sharing, AutoScout24 utilized ChatGPT to create internal documentation assistants. Instead of digging through outdated wikis or interrupting senior engineers, new hires can query an AI assistant to understand specific internal APIs, deployment procedures, or architectural standards.

This conversational interface has fundamentally changed how knowledge is distributed within the company, reducing developer onboarding times from months to weeks.

The integration of OpenAI’s models has yielded measurable improvements across AutoScout24’s engineering organization:

  • Increased Developer Velocity: Teams report significant time savings on routine coding tasks, allowing them to ship features to production faster.
  • Higher Code Quality: Automated test generation and real-time code suggestions have led to cleaner, more standardized codebases with fewer runtime errors.
  • Improved Developer Satisfaction: By automating repetitive boilerplate work, engineers can focus on creative problem-solving, leading to higher job satisfaction and lower burnout rates.

"AI hasn't replaced our developers; it has supercharged them," says an engineering lead at AutoScout24. "It allows our teams to stay in the 'flow state' longer, minimizing the friction of looking up syntax or writing repetitive tests."

AutoScout24’s success with Codex and ChatGPT is just the beginning. The company is currently exploring advanced use cases, including autonomous debugging agents, automated code review pipelines, and deeper integrations of LLMs into their customer-facing products to personalize the car-buying journey.

As AI models continue to evolve, AutoScout24’s proactive adoption serves as a powerful case study for how legacy enterprises can successfully integrate generative AI to build a faster, more resilient engineering culture.