AI-MSL packages are delivered as flat monthly services. Pricing is determined after the System Intelligence Assessment — based on your system's real architecture, not assumptions.
Usage of multiple AI models — Claude Code, Codex, and Gemini — included in every package.
Experienced engineering and AI professionals supervise every lifecycle stage.
A dedicated AI-MSL Service Manager who learns your system and works with your leadership.
Lifecycle governance and quality controls at every stage of development.
Continuous system context capture and analysis through AppGraph.
Personalized service designed for your organization's specific needs.
Choose how much of your software lifecycle
to move into AI-powered development.
Product definition and requirements intelligence grounded in real system context.
Finalized PRD ready for development teams or structured brief for AI-powered development.
AI-powered software development lifecycle. Requirements become production-ready code.
Everything in PM, plus:Features delivered next day — clean code, tests, docs, and a Git branch ready to review.
The entire software lifecycle managed by AI-MSL — from requirements to production operations.
Everything in Build, plus:Production updates delivered instantly with AI-powered deployment, monitoring, and cost optimization.
Deep integration with internal engineering processes and governance.
AI-MSL operates alongside your internal team with full governance alignment.
Pricing is determined after the System Intelligence Assessment — which builds AppGraph and analyzes your system architecture, dependencies, complexity, and AI-readiness.
Understand whether your system is a good fit for AI-MSL and what the expected cost will be. Build AppGraph, analyze architecture, determine AI-MSL service pricing.
Everything in Readiness, plus deeper architecture analysis, technical debt identification, modernization opportunities, and improvement roadmap.
Full long-term cost and sustainability analysis. Includes maintenance projections, operational risk analysis, team dependency evaluation. Ideal for acquisitions, investment analysis, or vendor transitions.
The assessment builds AppGraph — a semantic model of your software system — and produces a structured analysis of architecture, dependencies, quality, and operational complexity.
You receive a clear, data-driven understanding of your system and what AI-MSL service packages will cost.
AI-MSL is not about helping developers code faster — it replaces the need to manage development altogether through an end-to-end AI-powered lifecycle with governance.
When teams use tools like Claude Code directly, they often face:
AI-MSL solves this by operating on a system-wide context (AppGraph) and enforcing a governed lifecycle with supervision, ensuring all changes remain coherent, validated, and production-ready.
You can — but that model still depends on people coordinating, interpreting requirements, reviewing code, and managing releases.
AI tools improve individual productivity, but not:
AI-MSL removes dependency on developer coordination by introducing system-wide context awareness, end-to-end lifecycle execution, and expert supervision at key decision points.
This shifts from team-driven execution to system-driven lifecycle.
You retain full ownership of your code, repositories, and all generated assets.
AI-MSL operates on your system in a similar way to a development vendor or internal team — but with full traceability, structured changes, and consistent lifecycle governance. There is no lock-in to proprietary formats or hidden dependencies.
AI-MSL follows the same or stricter security model as working with a trusted engineering team or MSP.
In practice, this is comparable to — and often more controlled than — giving access to remote developers.
You need access to your system and the ability to describe your goals.
AI-MSL builds system understanding from your existing assets and improves it over time. You don't need perfectly structured documentation — the system evolves its understanding as it works with your codebase.
Even without moving into full development, the assessment stage provides immediate value:
This replaces uncertainty with a clear, data-driven baseline for decision-making.
You define your vision or high-level feature goals.
AI-MSL works with you to refine requirements, expand all use cases (not just happy paths), and evaluate impact across the entire system.
Once finalized, the development process runs automatically through AI agents and is supervised by AI Lifecycle Engineers, who intervene when needed and resolve issues early.
No. While you have full visibility into all stages and can interact with the system directly, AI-MSL is delivered as a platform + managed service.
You are supported by an AI Lifecycle Manager who understands your system and goals, and AI Lifecycle Engineers who oversee execution. This ensures that AI-driven development stays aligned with your product, architecture, and business objectives.
Yes. Standard packages are delivered through CloudGeometry-managed infrastructure, but enterprise deployments can run in your own VPC or environment, fully configured to your security and compliance requirements.
AI Lifecycle Engineers continuously monitor the development process using quality, correctness, and confidence signals at each stage.
When signals are low, they intervene, review outputs, adjust execution, and rerun lifecycle steps.
Over time, as the system learns your codebase, accuracy improves and manual intervention decreases.
Conceptually, it's similar to having a vendor manage your product lifecycle — but fundamentally different in execution.
You are also supported by an AI Lifecycle Manager, similar to a Technical Account Manager — but backed by an AI-driven execution system.
You get a fully structured and validated PRD, including complete requirements, all use cases (including edge cases), and system impact analysis.
This is generated in hours instead of weeks and can be used with or without continuing into development.
You receive a repository with implemented changes, tested and validated code, and updated documentation.
All changes are ready to be merged into your main system and deployed.
Start with a single application or system component. Run it through AI-MSL, implement a few changes, and compare speed, cost, and quality.
Most organizations see clear differences within days.
You can exit at any time. You retain your full codebase, improved documentation, structured system understanding, and identified modernization opportunities.
Your system will typically be in a cleaner and more maintainable state than before.
AI-MSL is designed to absorb and adapt to AI advancements, not depend on a single model or tool. We continuously integrate improvements from technologies like Claude, Codex, and others into the platform.
This means you don't need to track AI changes yourself, your system automatically benefits from improvements, and cost and performance improve over time as underlying tech evolves.
AI-MSL is not a tool — it's a lifecycle system built on top of evolving AI capabilities.
That is the direction AI-MSL is designed to achieve. The platform enables a closed feedback loop, where production signals (usage, performance, issues) are turned into improvements, optimizations, and fixes — automatically fed back into the development lifecycle.
For systems built from scratch using AI-MSL, this level of automation is already achievable. For existing systems, we apply the same framework progressively: introducing system context (AppGraph), adding lifecycle governance, and enabling feedback-driven improvements.
Human supervision (AI Lifecycle Engineers and Managers) ensures correctness while the system evolves toward increasing levels of autonomy.
Every system is different, so pricing is based on an initial automated assessment that evaluates system complexity, code quality, architecture and dependencies, and maintainability and risk.
Based on this, we estimate the cost of building and maintaining the system intelligence layer (AppGraph), ongoing maintenance and PM support, and expected cost ranges for future feature development.
In many cases, feature-level costs can be estimated early — even during requirements definition.
The model is fundamentally different. With a traditional team, you pay for continuous developer capacity, even when no changes are being made.
With AI-MSL, you pay a baseline maintenance cost to keep the system understood, monitored, and ready. You don't pay for idle development capacity — you only incur additional cost when new features or changes are implemented.
At the same time, your system remains fully documented, structurally understood, and ready for immediate extension. This reduces both cost and operational overhead.
The PM package allows you to go from an idea to a fully structured and complete PRD in hours. You describe what you want at a high level, and AI-MSL expands all use cases (including edge cases), validates requirements against your system, and ensures completeness and consistency.
The result is a production-ready requirements document that can be used within AI-MSL or by any external team — without additional clarification cycles. The cost of this capability is included in your assessed maintenance scope.
Feature cost is determined once requirements are defined — and often estimated even earlier. Pricing depends on feature complexity, system complexity and code quality, scope of impact across the system, and expected level of manual supervision required.
Costs include infrastructure and AI execution (LLM usage) and fractional involvement of AI Lifecycle Engineers.
As your system improves and AI-MSL becomes more tuned to it, fewer interventions are needed, execution becomes more efficient, and costs typically decrease over time.
The System Intelligence Assessment gives you a clear, data-driven understanding of your system and what AI-MSL service packages will cost.
