Before AI-MSL begins operating a software system, we perform a System Intelligence Assessment. This step builds AppGraph, analyzes your architecture, and determines exactly what AI-powered development will cost for your system.
From small web applications to large enterprise platforms with dozens of layers and integrations — every system has a different architecture, dependency structure, and operational complexity.
AI-MSL operates across a wide range of software systems. The System Intelligence Assessment ensures the platform operates based on the real architecture of your system — not assumptions, estimates, or team size.
This is why pricing is determined after assessment, not before. Your system's actual complexity determines the service cost.
What the Assessment Reveals
The assessment begins by building AppGraph — a semantic model of your software system that captures and organizes everything AI needs to work on your code safely.
The more system context that is available, the deeper and more accurate the analysis becomes. AI-MSL Technical Managers can conduct semi-automated interviews with your key system experts to capture tribal knowledge that doesn't exist in documentation.
AppGraph doesn't just collect data — it analyzes your system across five critical dimensions that determine how safely and efficiently it can evolve.
Architecture layers, service boundaries, module relationships, external integrations. Understanding structure reveals how safely the system can evolve.
Architectural consistency, coding patterns, duplication, technical debt, structural fragmentation. These determine how efficiently AI-driven development can operate.
Blast Radius analysis — how far a change propagates through the system. Maps dependencies across modules, services, and integrations to identify safe zones and high-risk areas.
How difficult different types of work will be — adding features, extending functionality, integrating services, modernizing components. Predicts realistic development effort.
Context completeness, architecture clarity, documentation coverage, test availability, development traceability. Determines how effectively the AI lifecycle can operate.
Even before starting AI-MSL services, the assessment provides significant value. For many organizations, this is the first time they gain a complete, structured understanding of their system.
Quantified maintainability scores across your codebase and architecture layers.
How complex each area of your system is and where that complexity comes from.
A complete picture of how modules, services, and integrations relate to each other.
Concrete areas where the system can be simplified, updated, or improved.
Where debt is accumulating, its severity, and the cost of addressing it.
What it will cost to maintain the system as-is over time — versus the AI-MSL alternative.
Choose the level of analysis that matches your goals.
Understand whether your system is a good fit for AI-MSL and what the expected cost will be.
Choose this when you want a clear, data-driven decision about adopting AI-powered development.
Everything in Readiness, plus deeper architecture analysis, technical debt identification, and improvement roadmap.
Choose this when you want to improve your system while transitioning to AI-powered development.
Full long-term cost and sustainability analysis for acquisitions, investment analysis, or vendor transitions.
Ideal for acquisitions, investment analysis, vendor transition planning, or strategic technology decisions.
Every deliverable included in each level of System Intelligence Assessment.
What happens when the assessment is complete
and you're ready to move forward.
You receive a comprehensive analysis of your system's architecture, complexity, and AI-readiness.
Based on the assessment results, choose the AI-MSL service package that fits your goals — PM, Build, Operate, or Enterprise.
Your dedicated Technical Manager learns the system deeply. AppGraph is expanded. Governance is configured.
AI-MSL begins operating your software lifecycle — from requirements to production code, governed at every stage.
Answers to the questions we hear most often before organizations start the assessment process.
Yes. Every system is different, and the System Intelligence Assessment ensures the platform operates based on the real architecture of your system — not assumptions. This is what makes pricing accurate and operations safe.
The duration depends on system size and complexity, but typically ranges from several days to a few weeks. Larger systems with more integrations and components take longer to analyze thoroughly.
No. AppGraph captures information from code repositories, documentation, infrastructure configurations, and other system sources automatically. Your team can also provide additional context through expert interviews.
Read-only access to code repositories is the primary requirement. Additional context sources — documentation, architecture diagrams, infrastructure configs — improve the analysis but are optional.
No. As the system evolves and code changes occur, AppGraph continuously updates to reflect those changes. Over time it becomes a living representation of the true state of the system.
Yes. The assessment provides immediate, standalone value — system maintainability metrics, complexity scores, dependency maps, and modernization opportunities. Many organizations find this useful regardless of next steps.
System Intelligence Assessment — understand your system before you commit.
Schedule Evaluation →The System Intelligence Assessment gives you a clear, data-driven understanding of your system and what AI-MSL service packages will cost.
AI-MSL is designed for organizations with existing production software. If you're building from scratch, this probably isn't the right fit.
