Knowledge Graph Construction from Visual Workflows: Mapping 1,500+ System Nodes for Engineering VPs

Every Engineering VP has a "dark matter" problem: thousands of lines of undocumented COBOL, Delphi, or Silverlight code that run mission-critical operations but remain invisible to modern architectural tools. When documentation is missing—which occurs in 67% of legacy systems—modernization efforts stall. You aren't just fighting technical debt; you are fighting an information vacuum.

The traditional approach to discovery involves manual code audits and stakeholder interviews, taking an average of 40 hours per screen. For an enterprise application with 50+ screens, you’re looking at months of "discovery" before a single line of React is written. This is why 70% of legacy rewrites fail or exceed their timelines.

By pivoting to automated knowledge graph construction from visual workflows, enterprise architects can map 1,500+ system nodes—components, state transitions, and API contracts—in a fraction of the time. Replay transforms this process from a manual archeology project into a high-speed automated pipeline.

TL;DR: Manual legacy discovery is the primary bottleneck in enterprise modernization, contributing to a $3.6 trillion global technical debt. Replay uses Visual Reverse Engineering to automate knowledge graph construction from video recordings of user workflows. This process reduces discovery time from 18 months to weeks, mapping thousands of system nodes into documented React components and structured design systems with 70% average time savings.

The Architecture of "Dark Matter" Systems#

Legacy systems are rarely modular. They are monolithic tangles where UI logic, business rules, and data fetching are tightly coupled. According to Replay's analysis, the average enterprise system contains over 1,500 distinct "nodes"—discrete units of functionality ranging from a specific validation rule on a form to a complex multi-step financial reconciliation flow.

Knowledge graph construction from these systems requires a multi-modal approach. You cannot rely on source code alone because the "source of truth" often resides in the runtime behavior that users interact with daily.

Visual Reverse Engineering is the process of extracting UI logic, state management, and component hierarchies directly from screen recordings of user interactions, bypassing the need for perfect source code documentation.

The Cost of Manual Discovery vs. Automated Mapping#

How Knowledge Graph Construction From Visuals Works#

To map 1,500+ nodes, you need a structured ingestion engine. Replay’s "Flows" feature allows engineers to record real user workflows. As the user clicks through a legacy Insurance Claims portal or a Banking Dashboard, the platform analyzes the pixel changes, DOM mutations (if web-based), and state transitions.

Step 1: Node Identification#

The first phase of knowledge graph construction from visual workflows is identifying atomic units. A "node" in this context isn't just a button; it’s a functional entity.

• •Visual Nodes: UI components (Inputs, Modals, Data Grids).
• •Logic Nodes: Conditional branches (e.g., "If user is in CA, show tax field").
• •Data Nodes: API endpoints and payload structures inferred from the UI behavior.

Step 2: Relationship Mapping#

Once nodes are identified, the graph must define the edges. If a user clicks "Submit" and a "Success" toast appears, the graph creates a directed edge between the

node and the node, mediated by a event.

Industry experts recommend focusing on "Business Capability Mapping" during this phase. Instead of just copying the code, you are mapping what the system does.

typescript
// Example of a System Node Definition in a Replay-generated Knowledge Graph
interface SystemNode {
  id: string;
  type: 'COMPONENT' | 'ACTION' | 'DATA_SOURCE';
  metadata: {
    legacyRef: string; // Original screen ID
    visualHash: string; // Used for UI consistency checks
    frequencyOfUse: number;
  };
  properties: {
    state: Record<string, any>;
    props: Record<string, string>;
  };
  edges: Array<{
    targetNodeId: string;
    trigger: 'CLICK' | 'HOVER' | 'SYSTEM_EVENT';
    condition?: string;
  }>;
}

const claimSubmissionNode: SystemNode = {
  id: "node_8821_claim_btn",
  type: "ACTION",
  metadata: {
    legacyRef: "SCR_004_CLAIM_ENTRY",
    visualHash: "a7b2...f31",
    frequencyOfUse: 0.95
  },
  properties: {
    state: { isLoading: false },
    props: { label: "Submit Claim", variant: "primary" }
  },
  edges: [
    {
      targetNodeId: "node_9912_success_modal",
      trigger: "CLICK",
      condition: "api_response_code === 200"
    }
  ]
};

Scaling to 1,500+ Nodes: The Engineering VP’s Playbook#

Mapping a handful of screens is easy. Mapping an entire ERP or EHR system is a scaling challenge. When performing knowledge graph construction from large-scale visual data, Replay uses an AI Automation Suite to group similar patterns.

1. Pattern Recognition and Component Normalization#

In a 1,500-node system, you likely have 50 different versions of a "Date Picker." Replay’s Library (Design System) tool identifies these visual duplicates and collapses them into a single, reusable React component. This prevents the "snowflake" problem where every screen has bespoke code.

2. Automated Flow Documentation#

Instead of writing READMEs that no one reads, the knowledge graph generates "Flows." These are interactive architectural diagrams that show how data moves through the system. For a VP of Engineering, this is the "GPS" for the modernization journey.

Modernizing Legacy Flows requires understanding the dependency graph before you start refactoring.

3. Blueprint Generation#

The "Blueprints" editor in Replay allows architects to tweak the discovered graph before it is exported to code. If the legacy system had a sub-optimal user flow, you can modify the node relationships in the graph before the AI generates the final React components.

Implementation: From Graph to Production React Code#

The ultimate goal of knowledge graph construction from visual workflows is the generation of clean, maintainable code. Below is an example of how a node in the graph is translated into a documented React component using Replay’s AI engine.

tsx
import React from 'react';
import { useClaimSubmit } from '../hooks/useClaimSubmit';

/**
 * Node: node_8821_claim_btn
 * Discovered from: Claim Entry Screen (SCR_004)
 * Logic: Triggers validation before POST /api/v1/claims
 */
export const SubmitClaimButton: React.FC<{ claimId: string }> = ({ claimId }) => {
  const { submit, isLoading, error } = useClaimSubmit();

  const handleClick = async () => {
    const success = await submit(claimId);
    if (success) {
      // Replay identified this transition to the Success Modal
      window.dispatchEvent(new CustomEvent('OPEN_SUCCESS_MODAL'));
    }
  };

  return (
    <button 
      className="bg-blue-600 text-white px-4 py-2 rounded shadow-sm hover:bg-blue-700 disabled:opacity-50"
      onClick={handleClick}
      disabled={isLoading}
    >
      {isLoading ? 'Processing...' : 'Submit Claim'}
    </button>
  );
};

This code isn't just a guess; it is grounded in the visual evidence captured during the recording phase. By utilizing knowledge graph construction from actual usage, the generated code respects the edge cases that manual rewrites often miss.

Why This Matters for Regulated Industries#

For VPs in Financial Services, Healthcare, and Government, "moving fast and breaking things" isn't an option. You need an audit trail.

• •SOC2 & HIPAA Compliance: Replay is built for regulated environments, offering on-premise deployments so your visual workflows never leave your secure perimeter.
• •Documentation as a Side Effect: Since the knowledge graph is the source of truth, documentation is generated automatically. You no longer have to choose between shipping features and updating the wiki.

According to Replay's analysis, teams using automated discovery are 3x more likely to pass internal architecture reviews on the first attempt because they can prove the new system matches the legacy system's functional requirements.

For more on building robust systems, see our guide on Component Library Automation.

The Strategic Advantage of Visual Reverse Engineering#

When you utilize knowledge graph construction from visual workflows, you are essentially building a digital twin of your legacy application. This digital twin allows you to:

1. •Identify Dead Code: If a node in the graph is never reached during 100 recorded user sessions, it’s a candidate for deletion rather than migration.
2. •Estimate with Precision: Instead of "guesstimating" a 2-year timeline, you can see exactly how many nodes exist and track the migration progress of each node in real-time.
3. •Bridge the Gap: Product Managers can look at the "Flows," while Engineers look at the React code, both of which are derived from the same underlying knowledge graph.

Knowledge graph construction from visual data is the only way to tackle the $3.6 trillion technical debt crisis without adding to it. By automating the discovery phase with Replay, you ensure that your modernization project is part of the 30% that succeed.

Frequently Asked Questions#

What is the primary benefit of knowledge graph construction from visual workflows?#

The primary benefit is the dramatic reduction in discovery time. By automating the mapping of system nodes (UI, state, and data) from video recordings, organizations can save up to 70% of the time usually spent on manual code audits and stakeholder interviews. This provides a clear, documented roadmap for modernization that is grounded in actual system behavior.

How does Replay handle complex data transitions in the knowledge graph?#

Replay’s AI Automation Suite analyzes the visual state changes and network calls captured during a recording. It identifies the "trigger" (e.g., a button click) and the "result" (e.g., a modal appearing or a data table updating). These are mapped as directed edges in the knowledge graph, ensuring that the generated React code preserves the complex business logic of the original system.

Can this approach work for systems with no existing documentation?#

Yes. In fact, that is the core use case. Since 67% of legacy systems lack documentation, Replay’s Visual Reverse Engineering is designed to create documentation from scratch by observing the system in production. It treats the legacy UI as the "source of truth," extracting the underlying architecture without needing to read a single line of the original, often obfuscated, source code.

Is the knowledge graph exportable to other architectural tools?#

Yes, the knowledge graph constructed from your workflows is structured data. While it is primarily used within Replay to generate React components and Design Systems, the node and edge data can be exported to inform enterprise architecture tools, Jira backlogs, or system diagrams, providing a comprehensive view of your technical landscape.

Ready to modernize without rewriting? Book a pilot with Replay