There is no room for error in Mediatech projects. Even a minor typo in the code can mean transmission downtime, problems with stream synchronisation, incorrect data displayed to viewers - all of which translate into image and financial losses. When working on a platform for live content and VOD, we were looking for a way to increase code quality without slowing down the development process. One of the solutions we tested was the implementation of Coderabbit - a tool that supports code review with the help of AI. Has it proved effective?

Let's start by defining the problem we wanted to solve.

#1 Problem: How to reduce code errors without slowing down development? ​

In the Mediatech industry, projects develop dynamically - often in a continuous mode, without longer 'stabilisation' phases. This creates some real challenges:

• Lack of time to manually review all code - especially in distributed teams.
• Differences in developer experience - juniors may not catch more subtle logic problems.
• Inconsistent coding style - makes it difficult to maintain and develop the system.
• Low quality feedback - often limited to technical comments, without reference to the context of the application.

The result? Bugs that go through code review end up in production - and cost money. Especially at Mediatech, where every second of delay or interruption is a real problem for users.

#2 Solution: Coderabbit as an "assistant" in the code review process ​

After testing, we decided to implement Coderabbit as a tool to support the code review process. Why?

Intelligent prompts and context analysis

Coderabbit not only detects syntax or formatting errors - it analyses the logic and structure of the code. If a condition in an if does not make sense in the given context of a function - we get a signal. If a function repeats itself in different files - Coderabbit will suggest refactoring it.

Personalised recommendations

The system learns the coding style of the team and adapts its suggestions. Juniors get more prompts, seniors only critical alerts.

Early error detection

With real-time feedback, errors are caught before the code reaches the reviewer. This reduces code review time and allows reviewers to focus on more complex aspects.

Better organisation

The tool suggests how to organise files, suggests naming conventions and indicates dependencies that are worth separating.

#3 Effects: What has the Mediatech project gained? ​

The implementation of Coderabbit has translated into concrete results:

• Reduction in production errors by 48%
• Reduction in code review by 35%
• Improved documentation and code consistency
• Greater team engagement - feedback from AI motivated to improve code quality

In an industry where quality translates into viewership and every glitch is a risk of losing the trust of the audience - it's an investment that pays off quickly.

Let’s talk business.

Reflection: AI as a partner, not a substitute ​

By implementing AI into Mediatech's processes, the aim is not to replace humans - but to support them at times when haste and complexity increase the risk of error. Coderabbit has shown that AI can act as a code mentor, providing not only technical feedback but also real-time education.

In dynamic projects, Mediatech is not an option - it's an advantage.