In live streaming projects, VOD platforms, recommendation systems – every millisecond of delay and every gap in test coverage can translate into real losses. When we were developing a new version of a streaming platform, we asked ourselves: how can we reduce development time without losing quality?

The answer? Automate as many elements of the process as possible – with the help of AI. We looked at the most effective tools to support developers in their daily work and tested their impact on the speed and quality of implementations.

#1 PROBLEM: How to reduce development time without losing quality? ​

The development teams at Mediatech work under great time pressure. The launch of a new platform or feature must be timed to coincide with a programme frame, an advertising campaign or the start of a sports season. This means minimal margin for error and fast iteration cycles. The most common issues reported by teams are:

• too much debugging and testing time,
• manual code analysis during review,
• lack of tools for intelligent code completion,
• delays in the implementation of new team members.

As a result, companies lose time, money and competitive advantage because developers have to focus on tasks that can be automated.

#2 PEOPLE: Where do teams lose the most time? ​

By analysing the development processes, we identified five areas with the greatest potential for optimisation:

1. Writing code – the lack of intelligent prompting support increases implementation time and syntactic errors.

2. Debugging and testing – manually sifting through code looking for bugs is hours of wasted time that could have been spent on feature development.

3. Code review – a manual process, burdening lead developers and delaying merge.

4. Documentation – a task often put off, even though crucial for onboarding and maintaining quality.

5. Optimisation and refactoring – rarely done on an ongoing basis due to lack of support in performance analysis.

Let’s talk business.

#3 SOLUTION: The most effective AI tools implemented at Mediatech ​

Based on the audit and tests carried out in Neoncube's projects in the Mediatech industry, the following is a list of the most useful tools supported by artificial intelligence.

• TabNine, CodeComplete, GitHub Copilot – suggest syntax, hint at variable names, functions and even whole sections of code in line with the project context.
• DebugMate, SonarQube, CodePro – analyse code using ML models, detect logic gaps, errors and point to specific fixes.
• Codacy, CodeScene, ReviewCode - tools that automate code review, pointing out duplicate code, inconsistencies in style and potential trouble spots.
• Doc.ai, Read the Docs, GitHub AI Docs – automatically generate feature descriptions, pull request summaries and changelogs.
• Applitools, Testim.io, Cucumber AI – generate test scenarios, automate test coverage, detect vulnerabilities in real time.
• SonarLint, CodeFactor, InteliSense – analyse application performance and suggest changes to improve speed and stability.
• Adobe XD AI, Figma AI Assist, Sketch AI – accelerate design iterations and integration of UX/UI teams with developers.
• ChatGPT, Dialogflow, BotStar – support developers in the IDE, help debug and suggest contextual solutions.

#4 RESULTS: What have we achieved through implementation? ​

Once the AI suite was fully implemented in the Mediatech project, measurable results were achieved:

• Reduction of production errors
• Reducing code review time
• Limitation of debugging time
• Faster onboarding of new developers
• Greater test coverage with the same team time

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#5 CONCLUSION: AI as the foundation for growth at Mediatech ​

Implementing AI tools in Mediatech projects not only speeds up development teams, but has a real impact on code quality and security. In a world where every second of delay can cost the loss of a user or advertiser, AI-based automation is becoming not an add-on but a necessity.

With AI, we can write better code, test it faster, document it more effectively and deploy it more securely. And that means – fewer failures, less stress and more time to develop real product value.