Success Story

The client is an e-commerce retailer offering a wide range of aftermarket automotive parts and accessories. The firm enables customers to find the right-fit parts through a digital-first, data-driven shopping and fulfillment experience.

The client aimed to strengthen its delivery approach and better support evolving business needs. They required a more structured, intelligent model to enhance speed, quality, and coordination across the SDLC. Their key requirements included:

• The need for end-to-end SDLC support to ensure continuity from development through testing, review, and release.
• Accelerating development and optimizing code quality without proportionally increasing engineering headcount.
• Improving product quality through intelligent, automated testing to reduce defects and production risks.
• Strengthening code reviews and release readiness to minimize rework and ensure predictable deployments.

Cybage implemented a structured GenAI-enabled SDLC framework to strengthen engineering discipline while accelerating scalable delivery.

• We enabled AI-driven code generation and optimization to automate boilerplate creation, generate structured SQL, and handle complex logic with cleaner, reusable outputs, reducing repetitive development effort.
• Our experts automated code comparison and refactoring intelligence to identify redundancies, inconsistencies, and optimization opportunities, improving long-term maintainability.
• By leveraging standardized, modular coding patterns, we simplified nested logic into easier-to-review components, enhancing readability and scalability.
• We introduced AI-assisted PR reviews, improving review quality, reducing friction, and strengthening collaboration between developers and reviewers.
• With test-ready engineering structures, we ensured automation-friendly code that enhanced unit test coverage and release readiness. This framework enabled practical, enterprise-scale AI adoption while supporting faster, more reliable software delivery.

• 15% reduction in boilerplate coding time achieved, accelerating development cycles and improving time-to-market for new releases.
• Improved code consistency and readability, resulting in faster onboarding and lower long-term maintenance costs.
• Reduced dependency on lead developers enabling better resource utilization and improved team scalability.
• Increased pull request review velocity with fewer errors, shortening release cycles and enhancing delivery predictability.
• Lowered defect rates and improved test coverage for new features, strengthening the product quality and reducing post-release rework.