There’s a lot of passion around AI right now with copilots, LLMs, MCP, generative tools that can purportedly modernize or rewrite code automatically. Most of us have heard enterprises say, “We’re experimenting with AI to accelerate modernization.”
But when you dig into what they’re doing, you often find a few scripts, some documentation parsing, and many disappointing results. The reason is simple: AI is brilliant with language, but completely unaware when it comes to software architecture. It needs to digest large volumes of code and interactions to really put its head around the architecture of an application. Moreover, it needs to be certain about the exact explicit and implicit dependencies within the system structures.
In simple words:
AI can read code, but it can’t understand systems. It doesn’t know that a COBOL program talks to a CICS transaction, which accesses a DB2 table, which is read by a nightly batch job that sends data to an SAP interface. It just sees words.
And that’s where the software intelligence by CAST completely changes the equation.
CAST provides AI with context
When CAST analyzes software codebase, it doesn’t just scan files. It builds a structural model of the system – how the program elements, data, transactions, and technologies interact. Using deterministic methods, not guessing it.
That model is the missing ingredient that makes AI relevant for modernization.
Think of it this way:
| AI Alone | CAST + AI |
|---|---|
| Sees code as text | Sees code as a system |
| Can summarize but not reason | Can reason based on structure |
| Produces plausible suggestions | Produces traceable, evidence-based insights |
One analogy I like is that CAST gives AI the map, while AI provides the narration. AI can explain, summarize, and hypothesize but only if it knows where things are. CAST is the part that tells it where everything is how things connect. This is where the new MCP server feature of CAST becomes key to feed AI the context it needs using a standardized protocol.
From code to clarity
CAST generates a rich dataset, a kind of “software knowledge graph” showing dependencies, data flows, technologies, and architecture layers. That’s what makes it so powerful when combined with an AI model or agent. Suddenly, AI is not just talking about code in the abstract, it’s talking about your system, your risks, your dependencies.
That’s a huge shift.
It transforms AI from a nice demo toy into something that can participate in real engineering or modernization conversations.
Imagine asking a chatbot:
“Which components in our insurance policy app depend on DB2?”
“Which modules would be easiest to containerize first?”
“Can you generate an executive summary of our portfolio modernization priorities?”
If that chatbot is backed by CAST software intelligence, it can answer all three with accuracy and context.
The integration
So how does this work in practice? There are a few integration paths, depending on how mature the organization’s AI strategy is.
In some cases, CAST exports data either through the CAST REST API, CSV, or GraphQL and that becomes part of a retrieval layer for an AI model. The Large Language Models (LLM) doesn’t have to invent information anymore; it retrieves facts from CAST.
In other setups, CAST feeds those data to AI using Model Context Protocol (MCP), which allows any MCP-aware AI agent or tool to reason over a portfolio or a particular application: recommend sequencing, identify hotspots, or even draft modernization plans based on dependencies.
And in some enterprise scenarios, companies integrate CAST data directly into their own internal knowledge graphs or RAG pipelines. That’s where things get extremely interesting because CAST becomes part of the company’s “AI memory.” Their copilots can accurately answer questions about systems using verified data instead of heuristics.
The kind of data AI consumes
The strength of combining AI with CAST lies in the variety and the precision of the software intelligence CAST provides. Here’s a simple way to look at it:
| Type of Intelligence | What It Describes | How AI Uses It |
|---|---|---|
| Application structure | Programs, components, and how they link | Understand what exists and how it’s built |
| Dependencies | Calls, data access, cross-system flows | Detect impact zones and migration blockers |
| Technology stack | Languages, databases, frameworks | Classify modernization paths |
| Structural metrics | Complexity, coupling, technical debt | Prioritize based on effort and risk |
| Data lineage | From source to destination | Explain compliance or business impact |
| Change history | Velocity and churn | Identify stable vs. volatile components |
This is what makes the context provided by CAST so crucial. It gives AI credibility. AI can’t hallucinate this data, it’s factual. It’s deterministically derived straight from the source code and architecture itself.
Demonstrating the value
You don’t need a complex integration to show this in action. Even simple scenario can make the point instantly.
Take a small CAST analysis result, a JSON export or even the dashboard. Feed a few data points into an AI assistant like ChatGPT, Claude, Gemini or an internal GPT. Then ask something practical:
“Which applications are most suitable for cloud replatforming first?”
“Summarize the main risks identified in the portfolio by business domain.”
Voilà. In seconds, the AI will generate a structured, human-readable explanation that ties directly to CAST’s findings. It doesn’t replace CAST, it translates CAST into human language. For quick proof of value, that’s powerful.
It shows not only what CAST does, but what you can do with CAST once integrated into the broader AI strategy.
Why it matters now
Right now, most enterprises are under pressure to “use AI everywhere and now”, including in their software development, maintenance, and modernization efforts. But many struggle to connect AI to something tangible. CAST gives them that anchor.
- It makes AI’s outputs trustworthy because they’re based on verified architecture data.
- It makes CAST’s insights accessible because AI can explain them in natural language.
- It makes modernization faster because AI can synthesize CAST data into plans, summaries, and recommendations.
Executives get to tell their boards: “We are using AI to accelerate modernization” and it’s true but in a way that’s explainable, safe, and data driven.
A broader perspective
If you think about it, CAST and AI are solving opposite halves of the same problem. CAST excels at precision; AI excels at communication. CAST builds the technical truth. AI gives that truth a voice.
Together, they create a bridge between deep system knowledge and human decision-making.
That’s why companies see AI as force multiplier for CAST, making the insights from CAST visible, explainable, and useful to a broader audience. And vice versa, they are seeing CAST as force multiplier for AI, providing it with the structural context it needs to reason and make changes effectively on existing systems.
Every large organization experimenting with internal copilots or modernization assistants eventually realizes they need a trustworthy technical knowledge base.
CAST already is that.
