BotMan AI uses knowledge graphs for legacy modernization

BotMan AI uses knowledge graphs for legacy modernization

Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it.

BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, business logic and data models in a knowledge graph. That context helps agents interpret the system they are rebuilding, according to Alejandro D’Andrea (pictured), founder and chief technology officer of BotMan.

“Intelligence is not the problem anymore,” he said. “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.”

D’Andrea spoke with theCUBE Research’s John Furrier for theCUBE + NYSE Wired: AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how knowledge graphs support legacy modernization and give AI agents reusable business context. (* Disclosure below.)

How knowledge graphs support legacy modernization

BotMan AI uses knowledge graphs to represent how an application’s components and business rules relate to one another, according to D’Andrea. Its approach rebuilds the application’s architecture rather than simply translating old code into a newer language.

“It’s basically migrating from an old code base — Delphi, Visual Basic, Oracle Forms — and rewriting that same system into a new architecture,” he explained. “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.”

Beyond code migration, knowledge graphs can provide reusable context for AI agents. Mapping relationships when information enters the system can reduce the discovery work needed for subsequent queries, according to D’Andrea.

“You get, let’s say, a legal document,” he gave as an example. “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.”

Connecting unstructured information to recognizable entities gives agents another way to recover business context, according to D’Andrea. A person, project or product can link otherwise separate messages and documents.

“One of the things that worked for us in terms of processing all this unstructured information … is link[ing] them through entities,” he said. “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.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI Luminaries interview series:

(* Disclosure: TheCUBE is a paid media partner for theCUBE + NYSE Wired: AI Luminaries interview series. Neither Neo4j, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE