FinTech Software That Evolves. Compliance Intact.

Even without moving into full development, the assessment stage provides immediate value:

• A structured understanding of your system (AppGraph)
• Visibility into dependencies, risks, and complexity
• Identification of modernization opportunities
• Evaluation of system quality and maintainability
• Cost estimates for future maintenance and feature development

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.

• Requirements are finalized in minutes, not weeks
• Development and testing happen in hours
• Lifecycle is fully visible and traceable
• Execution does not depend on specific individuals

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.