---
title: Amazon Q Business Case Study: Accelerating Developer Productivity with AI-Powered Coding Assistance
client: TargetBay
description: Deployed Amazon Q for Developers across multiple IDEs to streamline code documentation, unit test generation, and refactoring — achieving full developer adoption in 44 days.
url: https://www.factualminds.com/case-study/amazonq/
category: GenAI
publishDate: 2025-03-10
updateDate: 2025-03-10
---

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

> Deployed Amazon Q for Developers across multiple IDEs to streamline code documentation, unit test generation, and refactoring — achieving full developer adoption in 44 days.

## Challenge: Scaling Developer Productivity Across Multiple IDEs

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: Deploying Amazon Q for Developers Across the Engineering Organization

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: Full Amazon Q Adoption in 44 Days with 30–50% Faster Development

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

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For more on [Amazon Q for Developers](/services/amazon-q-for-developers/) and AI-powered developer productivity on AWS, see our service page.

## Results

- **Developer Adoption**: 44 Days
- **Faster Code Development**: 30-50%
- **Documentation Time Reduced**: 40%
- **Post-Release Defects Reduced**: 35%

## AWS Services Used

- amazon-q-for-developers

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*Source: https://www.factualminds.com/case-study/amazonq/*