AI MSL
AI MSL turns plain-language requests into production-ready code branches with expert AI supervision, automating the full development lifecycle.
AI MSL at a glance
- Pricing
- Paid
- Key strengths
- Outcome-based DevCredits pricing tied to shipped work · End-to-end automation from request to production branch · Live semantic architecture map for safer modernization
About AI MSL
AI MSL transforms how software teams ship work by interpreting natural-language requests and converting them directly into production-ready branches. Instead of juggling tickets across multiple tools, teams describe what they need in plain English and receive complete deliverables: specifications, code, tests, and documentation, all ready for review and merge. This approach collapses weeks of coordination into a single governed workflow, freeing developers to focus on strategic decisions rather than repetitive execution.
What sets AI MSL apart is its AppGraph system intelligence, which maintains a live semantic map of architecture, APIs, workflows, and dependencies. This continuous awareness drives accurate impact analysis and safer modernization, so every change is understood in the context of the full system. Legacy applications can be incrementally upgraded for cloud, Kubernetes, and AI workflows without the risk of big-bang rewrites.
Every shipment is handled by AI under the supervision of AI Lifecycle Experts who validate architecture, correctness, and alignment with business goals. Pricing follows an outcome-based DevCredits model, so spend tracks actual shipped work rather than seat licenses or hours. The underlying platform operates on a SOC 2, HIPAA, and FedRAMP certified Kubernetes environment, delivering stronger observability, reliability, and cost control across multi-cloud deployments. The result is faster delivery, lower operational risk, and clearer accountability from idea to production.
Features
- Outcome-Based DevCredits Pricing: Pay per completed, governed change using DevCredits units so spend tracks actual shipped work.
- End-to-End Lifecycle Execution: Converts plain-language requests into specifications, code, tests, and documentation, then delivers production-ready branches for your review and merge.
- AppGraph System Intelligence: Maintains a live semantic map of architecture, APIs, workflows, and dependencies to drive impact analysis and safer modernization.
- AI Execution With Expert Supervision: Lets AI handle refinement, implementation, and testing while AI Lifecycle Experts validate architecture, correctness, and alignment with business goals.
- Multi-Cloud Managed Production Ops: Runs on a SOC 2, HIPAA, and FedRAMP certified Kubernetes platform that improves observability, reliability, and cloud cost control across providers.
- Modernization Without Rewrites: Uses continuous architecture mapping to upgrade legacy systems for cloud, Kubernetes, and AI workflows instead of risky, big-bang rewrites.
Pros
Cons
AI MSL Pricing Plans
Core (PM + Development)
Custom
Operate (PM + Development + Operations)
Custom
Enterprise
Custom
DevCredit Bundles (200 DCs)
Custom
DevCredit Bundles (500 DCs)
Custom
DevCredit Bundles (1,000 DCs)
Custom
