The Challenge

With nearly 600 Ansible Playbooks spread across 20–25 repositories, Computer Space faced a challenge of how to maintain code quality and standardization. Teams with varying levels of expertise contributed to the codebase, leading to inconsistent implementations and bottlenecks in manual reviews. Maintaining best practices was time-consuming, error-prone, and unsustainable. It became clear that further scaling without a robust quality control system would introduce unacceptable risks. A more standardized approach was essential.

The Solution

Computer Space chose Steampunk Spotter for its ability to enforce quality at scale, integrate seamlessly into daily workflows, and provide real-time feedback. Computer Space leveraged Spotter custom policies to create a comprehensive set of custom policies (their “white book”) that were tailored to the bank’s specific requirements, covering tagging, naming conventions, error handling, and loop labeling. Next, they started using Spotter directly in Visual Studio Code, since Spotter already includes VSC integration, allowing developers to get instant feedback as they write code. They then expanded usage to scan entire repositories, identifying deprecated modules and migration needs. Finally, they embedded Spotter into their Git workflow to apply consistent quality checks on both internal and client-developed code, with clearly defined development and production branches. Read the full Step-by-step guide on how Computer Space implemented Steampunk Spotter .

The Result

With Spotter, Computer Space was able to significantly improve efficiency and increase the number of automation projects from a few to more than 20. Developers now spend much less time on manual code reviews, and the onboarding of new team members has been accelerated thanks to automated guidance. The Spotter implementation enabled the creation of a centralized Automation Office that now coordinates automation across more than seven departments. Spotter enabled consistent quality control across all teams, automated enforcement of best practices, and real-time feedback during development. Centralized quality control made development more predictable and scalable. Clients experienced fewer production issues, shorter delivery times and a noticeable improvement in overall reliability of automation.