What is AI MSL
AI-MSL (AI-Managed Software Lifecycle) is an end-to-end AI-powered solution for maintaining, modernizing, and extending existing software systems. It is delivered as a managed service, operating directly on your codebase without lock-in or retained dev teams. The model uses AI to execute core lifecycle tasks—requirements refinement, specification, implementation, testing, and documentation—under expert supervision. Pricing is outcome-based, measured in DevCredits, with one credit per completed, governed change.
How to use AI MSL
- Submit a Change: Product managers, business stakeholders, or operational teams submit a high-level feature request, operational improvement, AI initiative, modernization idea, or customer-driven change into the AI-MSL Workspace.
- Scope & Cost: AI-MSL transforms the request into structured lifecycle requirements, implementation scope, architectural impact analysis, estimated delivery timeline, and DevCredits cost projection.
- AI-Driven Lifecycle Execution: Once approved, AI-MSL executes implementation through AI-driven lifecycle workflows covering architecture validation, development, testing, documentation, modernization, and deployment preparation under expert supervision.
- Production-Ready Delivery: AI-MSL delivers a production-ready branch prepared for merge into your repositories and deployment workflows.
Features of AI MSL
- Outcome-Based Model: No hourly billing; every change is fixed-cost and guaranteed, with a dedicated AI Lifecycle Manager, AI execution infrastructure, and expert supervision included.
- No Lock-In, No Runtime Dependency: All work happens directly on your codebase and repositories; no platform tie-in.
- End-to-End Lifecycle Execution: From new feature development to corrective & adaptive maintenance and modernization—submit high-level requirements and receive production-ready changes as new branches.
- AI Execution with Expert Supervision: AI performs core lifecycle tasks; AI Lifecycle Experts supervise critical stages for architectural integrity, correctness, quality, and business alignment.
- Flexible Deployment & Operations: Review, merge, and deploy changes independently, or use CloudGeometry's Operate package for fully managed production environments.
- AppGraph System Intelligence: Continuously maintained semantic model of architecture, APIs, workflows, and dependencies for automated impact analysis and modernization confidence.
- Governance by Design: Built-in audit, governance, and human supervision at every gate.
- Managed Production Operations: Kubernetes-native hosting across AWS, Azure, GCP, and DigitalOcean with up to 60% savings on cloud bills.
Use Cases of AI MSL
- Software Maintenance: Continuous and proactive product evolution with security monitoring, vulnerability remediation, and technical debt reduction.
- New Features Development: Expand products, connect platforms and data, automate manual processes, and ship customer-driven enhancements 3–10× faster.
- Application Modernization: Continuously evolve existing systems toward AI-driven cloud and operational architectures without risky, expensive rewrites.
- AI Transformation: Upgrade business processes with AI agents, workflows, and automation with built-in governance and human supervision.
- Managed Production Operations: Move toward self-improving systems with AI-powered root cause analysis, cloud cost optimization, and reliability improvements.
FAQ
Q: How is AI-MSL different from traditional development outsourcing?
A: AI-MSL is an accelerator, not a platform you are tied to. All work happens directly on your codebase and repositories—similar to working with an external engineering team, but with a fundamentally different execution model: AI performs core lifecycle tasks with expert supervision, and pricing is per completed change.
Q: Can I use AI-MSL without CloudGeometry hosting?
A: Yes, you can review, merge, and deploy changes independently. CloudGeometry's Operate package is optional for fully managed production environments.
Q: What does a DevCredit cover?
A: One DevCredit covers one completed, governed change, including requirements refinement, specification, implementation, testing, documentation, and expert review.
Q: Is there a lock-in?
A: No, there is no lock-in or runtime dependency. AI-MSL works directly on your existing codebase and repositories.
