Case study

Revolutionising Engineering Knowledge Discovery in Media with Google Gemini Enterprise

Media and Entertainment

Engineer working at dual monitors in modern office, supporting media knowledge discovery with AI tools.

Challenge

Engineering and QA teams at a media and technology group were facing significant hurdles due to the difficulty in retrieving essential information from disparate tools. The lack of a cohesive search process meant valuable time was wasted, affecting the team's ability to resolve incidents swiftly and effectively plan sprints. 

Outcome

We at Endava implemented a Gemini Enterprise pilot which enabled natural language search capabilities across various platforms to speed up information retrieval for faster decision-making.  

Close-up of software test results showing passed and failed code executions on a dark screen.

A unified approach to information drives decision-making 

When it comes to engineering and quality assurance, finding the right information quickly is crucial to maintaining efficiency and productivity. However, with data scattered across multiple platforms like Jira, Confluence, Outlook and Git repositories, teams often face challenges in locating technical decisions, documentation and task context. This fragmented search process can slow down incident resolution, sprint planning and onboarding new team members, ultimately impacting overall delivery efficiency. 

 

The results were as follows: 

  • 60% reduction in documentation search timeThe new system drastically cut down the time spent searching for documentation, allowing teams to focus more on productive tasks.
  • Automated retrieval of code and ticketsAutomation of these processes streamlined workflows and minimised manual effort. 
  • 30% faster onboarding: New team members could access the information they needed with ease, making the onboarding process smoother and more efficient. 

With over 50 users validating key use cases across backend, QA and DevOps workflows, the solution confirmed improved coordination and faster access to project knowledge. 

Developer reviewing and debugging code on a laptop with multiple monitors in a workspace.

Technical deployment 

We deployed the solution in a secure Google Cloud Platform (GCP) sandbox. By integrating six core systems and enforcing role-based access, they ensured a secure and efficient environment. Real-world test cases and golden data were used to validate the assistant's accuracy and completeness. We also provided a full setup, testing and a technical handover to support future scale-up. 

Smiling professional working on a laptop at a desk with colleagues conversing in the background.

Technology stack 

The implemented technology stack included: 

  • Gemini Enterprise

  • Google Workspace 
  • Jira Cloud 
  • Confluence
  • GitHub/GitLab 
  • Microsoft Outlook, OneDrive, SharePoint 
  • Vertex AI 

This comprehensive stack facilitated a seamless integration and ensured that the solution met the diverse needs of the engineering and QA teams. 

 

The implementation of Gemini Enterprise has revolutionised the way engineering and QA teams discover and utilise knowledge. By significantly reducing search times and automating retrieval processes, teams can now operate more efficiently, ultimately enhancing the overall delivery process. As companies continue to navigate complex technical environments, solutions like these provide a blueprint for achieving greater coordination and productivity. 

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