Amazon Q Business Case Study: Accelerating Developer Productivity with AI-Powered Coding Assistance

Challenge

TargetBay, a leading eCommerce marketing automation platform, sought to enhance developer productivity by leveraging AI-powered coding assistance. Their engineering team faced several obstacles that slowed delivery velocity and increased maintenance overhead:

• Seamless IDE Integration: Developers worked across multiple IDEs including Eclipse, Visual Studio, and JetBrains products. Any AI tooling needed to integrate consistently across all environments without disrupting existing workflows.
• Multi-Repository and Microservices Architecture: TargetBay’s codebase spanned numerous repositories and microservices, requiring a structured setup that could handle cross-repo context and service boundaries.
• Developer Training and Adoption: Effective use of AI coding assistants depends heavily on prompting skills. The team needed hands-on guidance to move beyond surface-level suggestions and unlock real productivity gains.
• Manual Code Documentation: Engineers spent significant time writing and maintaining documentation manually, diverting effort from feature development.
• Code Quality Concerns: Without automated unit test generation and structured refactoring support, post-release defects consumed engineering cycles and eroded confidence in the release process.

Solution

FactualMinds implemented Amazon Q for Developers across TargetBay’s engineering organization with a structured rollout designed for rapid adoption and measurable impact.

Amazon Q Plugin Setup and Configuration:

• Installed and configured the Amazon Q plugin across Eclipse, Visual Studio, and JetBrains IDEs, ensuring consistent behavior and feature parity across all development environments.

Multi-Repository Configuration:

• Set up Amazon Q to work effectively across TargetBay’s microservices-based architecture, enabling contextual suggestions that respect service boundaries and cross-repo dependencies.

Developer Training Sessions:

• Conducted hands-on workshops covering inline code suggestions, keyboard shortcuts, prompt engineering techniques, and best practices for integrating AI assistance into daily development workflows.

Code Documentation Automation:

• Enabled real-time documentation generation within the IDE, allowing developers to produce accurate, context-aware documentation as they write code rather than as a separate manual task.

Unit Test and Code Refactoring Optimization:

• Configured Amazon Q to generate unit tests aligned with existing test frameworks and to suggest targeted refactoring improvements that reduce complexity without introducing regressions.

Performance Monitoring and Optimization:

• Established ongoing support and fine-tuning processes to track adoption metrics, suggestion acceptance rates, and productivity indicators, ensuring continuous improvement.

Results

The deployment delivered rapid adoption and measurable productivity improvements across the engineering team:

• Full developer adoption in 44 days from initial rollout, with all active engineers using Amazon Q as part of their daily workflow
• 30-50% faster code development as inline suggestions and AI-assisted code generation reduced time spent on boilerplate and repetitive tasks
• 40% reduction in documentation time through automated, context-aware documentation generation
• 35% fewer post-release defects driven by automated unit test generation and AI-guided code refactoring
• Higher developer satisfaction as engineers could focus on complex problem-solving rather than routine tasks
• Reduced code review overhead for technical architects, who spent less time on manual reviews thanks to improved code quality at the source