BotMan AI Taps Knowledge Graphs to Decode Legacy Systems for Modernization

Modernizing legacy applications demands more than translating old code into new languages—it requires grasping how an application's components, data flows and business rules interconnect. BotMan AI addresses this challenge through an AI-driven migration pipeline that constructs a knowledge graph, capturing dependencies, business logic and data structures across the entire system. This contextual foundation allows AI agents to interpret and rebuild applications with genuine understanding of what they're working with, according to Alejandro D'Andrea, the company's founder and chief technology officer.

Intelligence is not the problem anymore. The problem is context. You have to explain to the agent everything it needs to understand [how] to do a particular task. What if you didn't have to explain that? What if it already knew all that you know? That's the key now.

Alejandro D'Andrea, founder and CTO of BotMan AI

D'Andrea shared these insights during an interview with theCUBE Research's John Furrier as part of theCUBE + NYSE Wired: AI Luminaries interview series, broadcast on theCUBE, SiliconANGLE Media's livestreaming platform. The conversation centered on how knowledge graphs enable legacy modernization and equip AI agents with reusable business context.

How knowledge graphs support legacy modernization

BotMan AI deploys knowledge graphs to capture the relationships between an application's components and its business rules, according to D'Andrea. Rather than performing a straightforward conversion of older code into contemporary languages, the approach fundamentally re-architects the entire system.

It's basically migrating from an old code base — Delphi, Visual Basic, Oracle Forms — and rewriting that same system into a new architecture. It's re-architecting the entire thing. We ingest the whole thing at once and we migrate it fully autonomously. That's where graphs play a big part.

Alejandro D'Andrea

Beyond enabling code migration, knowledge graphs furnish AI agents with reusable context that streamlines their work. When relationships between information are mapped as data enters the system, subsequent queries require less exploratory effort, D'Andrea explained.

You get, let's say, a legal document. At that point, when you get it, you can create all the relationships to other documents. You can say, 'This relates to this particular project and this particular loss and this particular lawsuit.' And you do that once. Then any agent, when you assign a task, will know automatically from the graph that all that information also might be relevant.

Alejandro D'Andrea

Anchoring unstructured information to concrete entities offers agents an additional mechanism for recovering business context. A person, project or product can serve as a bridge connecting disparate communications and records.

https://www.youtube.com/embed/BwxFXh0RVc0?feature=oembed

One of the things that worked for us in terms of processing all this unstructured information … is link[ing] them through entities. An entity for us will be a person, a project or a product. It will look you up, it will see all the connections that you already have, the projects that you're working on, the interactions that we had and everything. And then it will figure out where that piece of information … should fit.

Alejandro D'Andrea