AI-Driven Development Life Cycle

It is an End-to-End Transformation methodology for software development and operations transformation based on AI Code Assistant. AIR DevOps is applied throughout the entire development and operations process to maximize developer productivity and support efficient operations through automation.

1. AIR DevOps SDLC - Fast release through AI-based CI/CD/CO integration - Simultaneous quality and productivity assurance through automation and AI Assist - Integrated Dev·Ops·Sec, cloud-native scalability - Agile market response through real-time feedback 2. Existing development methodology - Sequential progress, delayed releases - Post-testing, manual-focused approach - Separated development and operations, increased scaling costs - Difficult change response and rework burden

1. Maximize development speed: Reduce coding time with AI automation (H Company case study: 30% improvement in code productivity) 2. Secure cost efficiency: Cost savings through shortened development periods

IR DevOps organically combines the latest cloud-native architecture and AI technology to provide the most suitable and flexible technology stack for your environment. First, in the application development area, we support the latest web frameworks such as React and Vue.js and hybrid app structures, and use enterprise standard languages such as Java, Python, and TypeScript in the backend. In particular, we integrate various AI coding tools such as Amazon Q Developer, Cursor, Claude, and GitHub Copilot to maximize development productivity. For infrastructure and cloud environments, we build container environments based on Kubernetes and Docker to implement auto-scaling, and enhance database availability through RDS, Redis Cluster, and more. In terms of operations and DevOps, we automate the build and deployment process through CI/CD pipelines via GitLab, and thoroughly manage code quality through static analysis using SonarQube and security checks. Additionally, we establish a monitoring system based on AWS CloudWatch, Whatap, and AI Ops to detect and predict system anomalies in advance, and help seamlessly transition legacy code to modern environments through AI analysis.

We apply a 4-stage methodology framework based on project management standards to systematically guide customers through their AI transformation (AX) journey. - Stage 1: Strategy consulting and AI Code Assistant selection - Stage 2: MVP validation (PoC) and practice-based internalization - Stage 3: Full deployment and AI automation SDLC integration - Stage 4: Intelligent operations based on AI Ops and completion of internalization

Yes, it is possible. We provide AI-based Code Modernization services. (Case study with N company: Achieved up to 82% conversion rate by analyzing and converting legacy Java and Node code using Agentic AI tools) We quickly and safely convert existing code to the latest architecture, eliminating technical debt and improving performance.

In the financial sector, we resolved outdated script issues and significantly improved development productivity by optimizing builds and advancing CI/CD pipelines to meet the industry's strict requirements. Additionally, we achieved enhanced operational stability through automated validation processes. In the retail/distribution platform sector, we optimized infrastructure to ensure stable services even during peak traffic periods and established an AI-based monitoring system to secure both operational stability and cost efficiency. Furthermore, we have successfully built platforms that enable flexible service expansion and intelligent operations even in complex integrated on-offline system environments.