Data Cosmos AutoDeployer– AI-Powered Repository to Kubernetes Deployment

Overview: Data CosmosTM AutoDeployer is a Coforge Data Cosmos accelerator that transforms any existing code repository into deployment-ready artifacts and a live Kubernetes deployment with minimal manual effort. AutoDeployer combines repository intelligence, AI-powered analysis, automated artifact generation, and GitHub Actions-based execution into a single governed workflow — eliminating manual creation of Dockerfiles, Compose files, Kubernetes manifests, and CI/CD pipelines. It accelerates application onboarding, modernization, and cloud delivery cycles while improving consistency across cloud-native applications. Part of Coforge Data Cosmos™ - the innovation backbone comprising of platforms, agents, and services that accelerates execution across every phase of the data lifecycle.

The Problem Today: • Complex Repositories: Hard to understand services, dependencies, and runtime needs • Manual Artifact Creation: Docker files, Compose, and Kubernetes manifests take significant time • Inconsistent Deployments: Manual config leads to errors and rework across environments • Slow Deployment Cycles: Multiple iterations across Dev, DevOps, and Platform teams • Troubleshooting Challenges: Difficult to diagnose failures across disparate tools and logs • Limited Governance: No standardized approvals, validation, or traceability

Core Capabilities:

1. AI-Powered Repository Analysis: Automatically discovers services, dependencies, ingress needs, runtime requirements, risks, and generates deployment recommendations. Understands application architecture without manual documentation review.
2. Automated Artifact Generation: Creates production-ready Dockerfiles, Docker Compose files, Kubernetes manifests (Deployments, Services, Ingress, ConfigMaps, Secrets), and GitHub Actions CI/CD pipelines tailored to the analyzed repository.
3. Human-in-the-Loop Governance: Repository, analysis, and artifact approvals at every stage. Platform teams retain control while automation handles heavy lifting. Approval gates enforce enterprise standards.
4. Deployment Readiness Validation: Preflight validation ensures artifacts are complete, consistent, and deployment-ready before execution. Catches configuration issues before they impact production.
5. Automated Deployment Execution: Push to GitHub, manage secrets securely, trigger workflows, and monitor deployments end-to-end. Integrates natively with Kubernetes clusters on Amazon EKS.
6. Full Traceability & Observability: Pipeline states, execution logs, and complete audit history for every deployment. Enables compliance reporting and rapid troubleshooting. 7-Step Workflow: • Project Creation: Provide repository details and deployment configuration • Repository Discovery: Fetch snapshot and inspect services, frameworks, and files • Repository Analysis: Analyzer generates insights, architecture, and deployment recommendations • Artifact Generation: Approve analysis and Builder creates deployment artifacts • Artifact Review: Review generated files and confirm deployment readiness • Deployment Execution: Push to GitHub, set secrets, trigger workflows, monitor status • Continuous Improvement: Modify artifacts, rerun deployment, and improve outcomes Key Value: • 60–80% faster application deployment with AI automation • 70% reduction in DevOps engineering and repetitive tasks • 90%+ deployment standardization with structured artifacts • 100% traceability with approvals, validations, and audit history • 30–50% improvement in DevOps productivity and efficiency Industry Applications: • Banking: Accelerate deployment of microservices for core banking, risk analytics, and regulatory reporting. Human-in-the-loop governance ensures compliance-ready artifacts for OCC examinations. • Insurance: Onboard claims, policy admin, and underwriting microservices to Kubernetes with standardized artifacts. Audit history supports Solvency II evidence. • Travel: Rapid deployment of booking, revenue management, and loyalty microservices during peak-season scaling. • Healthcare: HIPAA-compliant deployment of clinical microservices with governed artifacts, secure secrets management, and audit trails for PHI-handling applications. Cloud-Native Deployment on AWS: Deployed natively for Amazon EKS as target Kubernetes environment. Amazon Bedrock powers AI-driven repository analysis. Amazon ECR for container image registry. AWS Secrets Manager for secure secrets. GitHub Actions integration for CI/CD. Amazon CloudWatch for deployment monitoring.