Modernize legacy systems with AI-driven insights and automation for enhanced efficiency and reduced technical debt.
A Continuous Value Loop with Sirrus7 & Multi-Platform Capability
Existing codebase (Java , C# , Python ) serves as the foundation.
Automated CI/CD pipeline converts code to Abstract Syntax Trees (ASTs).
Generated ASTs are securely stored for analysis and LLM ingestion.
ASTs are fed into specialized LLMs for deep codebase understanding.
Leveraging LLMs for Code Transformation & Enhancement
Ingest Assess phase outputs: identified issues and modernization goals. Define LLM interaction strategy.
Strategize and prioritize fixes. LLM suggests refactoring paths, code snippets for upgrades. Human oversight is key.
Utilize LLMs to generate unit tests, integration tests, and stubs to improve code coverage.
Thoroughly review, test, and validate all LLM-assisted changes. Commit enhanced codebase.
Automating Infrastructure and CI/CD for Modernized Applications
Define target cloud environment. Design Infrastructure as Code using AI-assisted tools.
Design automated CI/CD pipelines with build, test, security scan, and deployment stages.
Package applications into containers and configure Kubernetes for scalability.
Execute blue/green deployments with health checks and automated rollback strategies.
Leveraging AI for Ongoing Excellence and Feature Enhancement
Implement AI-powered monitoring to track performance and detect anomalies in real-time.
Utilize LLMs and AI coding assistants for rapid prototyping and feature development.
Employ AI-driven auto-scaling based on predicted load and cost optimization.
Ongoing AI-assisted security scanning and vulnerability management.