{
  "id": 13847377,
  "title": "BotMan AI uses knowledge graphs for legacy modernization",
  "url": "https://urgent.news/2026/10/12/botman-ai-uses-knowledge-graphs-for-legacy-modernization",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-12T01:46:26.000Z",
  "source": {
    "name": "SiliconANGLE",
    "slug": "siliconangle",
    "url": "https://siliconangle.com/2026/10/11/botman-ai-uses-knowledge-graphs-for-legacy-modernization-neo4jdatatoknowledge/"
  },
  "original_language": "en",
  "account": "Legacy modernization relies on comprehending an application's code, data and business rules interplay before reconstruction. BotMan AI deploys an AI-driven migration pipeline that charts an application's dependencies, business logic and data models within a knowledge graph. This context enables agents to grasp the system they are rebuilding, as per Alejandro D'Andrea, BotMan's founder and chief technology officer. \"Intelligence is no longer the issue,\" he remarked. \"The issue is context. You must inform the agent about everything it requires to comprehend [how] to accomplish a specific task. What if you didn't have to explain that? What if it already knew all that you know? That's the crucial aspect now.\" D'Andrea discussed the role of knowledge graphs in facilitating legacy modernization and supplying AI agents with reusable business context during his appearance on theCUBE + NYSE Wired: AI Luminaries interview series with John Furrier. Knowledge graphs depict the relationships among an application's components and business rules, according to D'Andrea. The approach refactors the application's architecture instead of merely converting outdated code into a newer language. \"It fundamentally involves migrating from an older codebase — Delphi, Visual Basic, Oracle Forms — and rebuilding the same system using a new architecture,\" D'Andrea explained. \"We ingest everything at once and migrate it completely autonomously. That's where graphs truly shine.\" Knowledge graphs also enable AI agents to leverage reusable context beyond code migration. Mapping relationships when information enters the system can minimize the data discovery effort required for subsequent queries, according to D'Andrea. For instance, when handling a legal document, the system can create relationships to other documents, noting that they pertain to a particular project, loss, or lawsuit. \"Once you do this, any agent tasked with the job will automatically be aware, from the graph, that all that information might be pertinent as well,\" D'Andrea explained. Linking unstructured information to recognizable entities further aids agents in reconstructing business context, D'Andrea added. By connecting people, projects, or products to otherwise isolated messages and documents, the system can better understand where specific pieces of information belong. \"One of the ways we managed to process all this unstructured information is by linking them through entities,\" D'Andrea stated. \"An entity in our case would be a person, a project, or a product. It will look you up, see all the connections you already have, the projects you're working on, the interactions we've had, and then figure out where that piece of information should fit.\"",
  "summary": "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 […]…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}