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February 18, 2026 min readfaster knowledge graph construction

90% Faster Knowledge Graph Construction from Legacy UI Video Metadata

R
Replay Team
Developer Advocates

90% Faster Knowledge Graph Construction from Legacy UI Video Metadata

The most expensive asset in your enterprise isn’t your data center or your proprietary algorithms; it is the undocumented business logic trapped inside 20-year-old, COBOL-backed user interfaces that only a handful of senior employees know how to navigate. When these systems need modernization, the primary bottleneck isn't writing new code—it's the discovery phase. Traditional discovery relies on manual interviews and "archaeological" code reviews, a process that contributes to why 70% of legacy rewrites fail or exceed their timelines.

To break this cycle, architects are turning to Visual Reverse Engineering. By capturing real user workflows through video and extracting metadata, organizations can achieve significantly faster knowledge graph construction, turning opaque legacy "black boxes" into structured, actionable blueprints for modern React-based architectures.

TL;DR: Manual legacy discovery takes months and is prone to error. By using Replay to record user workflows, enterprises can extract UI metadata to automate the creation of knowledge graphs. This approach leads to 90% faster knowledge graph construction, reducing the average per-screen documentation time from 40 hours to just 4 hours.

The Crisis of Undocumented Technical Debt#

The global technical debt crisis has ballooned to an estimated $3.6$ trillion. For the average enterprise, this debt isn't just "messy code"—it's a complete lack of structural understanding. Industry experts recommend that before a single line of a rewrite is started, a comprehensive map of existing functional requirements must be established. However, according to Replay's analysis, 67% of legacy systems lack any form of up-to-date documentation.

When you attempt to modernize a system in a regulated environment—be it Financial Services, Healthcare, or Government—the cost of missing a single edge case in a workflow can be catastrophic. Historically, building a knowledge graph of these workflows required hundreds of hours of manual screen-scraping and interviews.

Video-to-code is the process of converting screen recordings of legacy software into structured metadata, component hierarchies, and functional specifications. By automating this, we move from manual "guesswork" to data-driven architectural planning.

The Architecture of Faster Knowledge Graph Construction#

To achieve faster knowledge graph construction, we must move away from static analysis of source code. Legacy source code often contains "dead" logic that hasn't been executed in a decade. Instead, we focus on runtime reality.

1. Metadata Extraction from Video#

When a user records a workflow in Replay, the platform doesn't just see pixels. It analyzes temporal changes, identifies recurring UI patterns, and maps user interactions (clicks, hovers, data entry) to specific functional outcomes. This metadata forms the "nodes" of our knowledge graph.

2. Entity Mapping and Relationship Discovery#

In a legacy system, a "Customer ID" field might appear on twenty different screens. Manual discovery would require a developer to find all twenty instances. Automated metadata extraction identifies these entities across the entire video library, creating "edges" in the knowledge graph that represent data flow and state transitions.

3. Structural Synthesis#

Once the entities and relationships are identified, the system synthesizes them into a Design System. This is where the 70% time savings truly manifest. Instead of a designer recreating a button or a complex data grid from scratch, the knowledge graph provides the exact specifications needed to generate a React component.

MetricManual DiscoveryReplay (Visual Reverse Engineering)
Time per Screen40 Hours4 Hours
Documentation Accuracy60-70% (Human Error)98% (Metadata Driven)
Timeline for 100 Screens18-24 Months4-6 Weeks
Knowledge TransferSubjective/InterviewsObjective/Graph-Based
CostHigh (SME intensive)Low (Automated)

Why Metadata is the Key to Faster Knowledge Graph Construction#

The traditional "rewrite" fails because it treats the legacy system as a static object. In reality, a legacy system is a series of state changes. Faster knowledge graph construction is possible only when you capture the intent behind those changes.

According to Replay's analysis, metadata extracted from video provides three layers of insight that source code cannot:

  1. •Temporal Context: How long does a user wait for a mainframe response?
  2. •Behavioral Heuristics: What "workarounds" do users employ that aren't in the official manual?
  3. •Visual Hierarchy: Which elements are actually important to the user vs. which are legacy clutter?

By feeding this metadata into a graph database, we can visualize the entire application architecture before writing a single line of TypeScript.

Implementation: Defining the Knowledge Graph Node#

To implement this, we represent each UI element as a node in a TypeScript-based schema. This allows the Replay AI Automation Suite to map legacy pixels to modern component properties.

typescript
interface UINode {
  id: string;
  type: 'button' | 'input' | 'grid' | 'nav';
  legacyLabel: string;
  observedWorkflows: string[];
  metadata: {
    coordinates: { x: number; y: number; w: number; h: number };
    frequencyOfUse: number;
    associatedDataField: string;
  };
  relationships: {
    triggersAction: string; // ID of another node
    parentContainer: string;
  };
}

const customerSearchNode: UINode = {
  id: "btn-001",
  type: "button",
  legacyLabel: "F3 - SEARCH",
  observedWorkflows: ["Customer Onboarding", "Account Retrieval"],
  metadata: {
    coordinates: { x: 450, y: 200, w: 100, h: 30 },
    frequencyOfUse: 0.85,
    associatedDataField: "CUST_ID"
  },
  relationships: {
    triggersAction: "api-call-search-01",
    parentContainer: "main-search-panel"
  }
};

This structured data allows for faster knowledge graph construction because the AI doesn't have to "guess" what a button does; it has the interaction history as evidence.

From Graph to React: Automating the Modernization#

Once the knowledge graph is populated, the transition to code becomes a compilation task rather than a creative one. We use the graph to drive the generation of a modern Design System and Component Library.

For example, a legacy "Data Entry Grid" identified in the knowledge graph can be automatically transformed into a high-performance React component using Tailwind CSS and Headless UI patterns.

Generated React Component Example#

The following code represents a modernized version of a legacy data grid, informed by the metadata stored in our knowledge graph.

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

/**
 * Modernized CustomerGrid
 * Generated via Replay Visual Reverse Engineering Metadata
 * Original System: IBM 3270 Terminal Emulation (Screen ID: CUST-402)
 */
export const CustomerGrid: React.FC = () => {
  const { data, loading, error } = useLegacyData('CUST_ID');

  if (loading) return <div className="animate-pulse h-64 bg-slate-200 rounded" />;
  
  return (
    <div className="overflow-hidden shadow ring-1 ring-black ring-opacity-5 sm:rounded-lg">
      <table className="min-w-full divide-y divide-gray-300">
        <thead className="bg-gray-50">
          <tr>
            <th className="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">Customer Name</th>
            <th className="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">Status</th>
            <th className="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">Last Active</th>
          </tr>
        </thead>
        <tbody className="divide-y divide-gray-200 bg-white">
          {data.map((customer) => (
            <tr key={customer.id}>
              <td className="whitespace-nowrap px-3 py-4 text-sm text-gray-500">{customer.name}</td>
              <td className="whitespace-nowrap px-3 py-4 text-sm">
                <span className={`inline-flex rounded-full px-2 text-xs font-semibold ${customer.active ? 'bg-green-100 text-green-800' : 'bg-red-100 text-red-800'}`}>
                  {customer.status}
                </span>
              </td>
              <td className="whitespace-nowrap px-3 py-4 text-sm text-gray-500">{customer.lastSeen}</td>
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This component isn't just a visual replica; it is functionally mapped to the legacy data fields identified during the faster knowledge graph construction phase. This ensures that the new system maintains 100% parity with the business logic of the old system.

Overcoming the "Documentation Gap" in Regulated Industries#

In sectors like insurance and healthcare, the "how" is just as important as the "what." Auditors require proof that the new system handles data exactly like the legacy one. This is where traditional manual modernization often falls apart.

Replay provides a "Blueprint" for every screen. These Blueprints are more than just design files; they are living documents that link the new React code directly back to the video evidence of the legacy system. If an auditor asks why a specific validation logic exists, the team can point to the node in the knowledge graph and the corresponding video timestamp.

The Role of AI in Scaling Discovery#

Manual knowledge graph construction is linear; the more screens you have, the more people you need. Faster knowledge graph construction via Replay's AI Automation Suite is exponential. As the AI records more workflows, it begins to recognize cross-application patterns.

For instance, if you are modernizing a suite of 50 internal tools, the AI will identify that the "User Authentication" pattern is identical across all of them. It marks these as "Shared Components" in the Library, preventing redundant work and ensuring a unified Design System.

Learn more about building automated design systems

Technical Implementation: The Metadata Pipeline#

To achieve the 90% speed increase, Replay utilizes a sophisticated pipeline that processes video metadata into a queryable graph.

  1. •Ingestion: Real user sessions are recorded. Unlike synthetic tests, these capture the "chaos" of real-world usage.
  2. •Normalization: The video is broken down into state changes. A "state change" occurs when the UI reacts to user input.
  3. •Entity Resolution: The AI identifies UI elements (OCR for text, CV for icons/buttons).
  4. •Graph Construction: Entities are linked based on sequential occurrence. If "Screen A" always leads to "Screen B" after clicking "Button X," an edge is created.
  5. •Code Export: The graph is exported as a set of React components and a documented Design System.

This pipeline is what makes faster knowledge graph construction a reality for the enterprise. Instead of waiting 18 months for a discovery report, architects get a functional blueprint in days.

Security and Compliance: On-Premise and Beyond#

For industries like Government and Telecom, sending video recordings of sensitive internal systems to a public cloud is a non-starter. Replay addresses this by offering On-Premise deployment and is built for regulated environments with SOC2 and HIPAA-ready configurations.

The metadata extraction happens within your secure perimeter. The knowledge graph stays under your control. This ensures that while you are achieving faster knowledge graph construction, you aren't sacrificing the security of your most sensitive business logic.

Frequently Asked Questions#

How does video metadata improve the accuracy of knowledge graphs?#

Traditional discovery relies on human memory or outdated documentation, both of which are fallible. Video metadata provides an objective, timestamped record of exactly how a system behaves in production. By converting these recordings into structured data, we eliminate the "interpretation gap," ensuring the knowledge graph reflects the actual state of the legacy system, not just the perceived state.

Can this approach handle terminal-based or mainframe legacy systems?#

Yes. Visual Reverse Engineering is platform-agnostic. Because Replay analyzes the visual output and user interaction metadata, it doesn't matter if the underlying system is a green-screen terminal, a Java Swing app, or an ancient PowerBuilder UI. If it can be displayed on a screen and recorded, it can be mapped into a knowledge graph.

Is the generated React code production-ready?#

The React components generated via Replay's Blueprints are designed to be "clean-room" implementations. They follow modern best practices, including TypeScript for type safety and modular CSS. While a developer will still perform a final review and integrate specific backend APIs, the components provide 80-90% of the boilerplate and UI logic, drastically accelerating the path to production.

How does "faster knowledge graph construction" impact the total cost of ownership (TCO)?#

By reducing the discovery phase from months to weeks, organizations save significantly on labor costs. More importantly, it reduces the "opportunity cost" of delayed modernization. According to Replay's analysis, the 70% time savings in discovery translates to a 40-50% reduction in the total project budget, as it prevents the common "re-work" cycles that occur when requirements are missed during manual discovery.

Conclusion: The End of Manual Discovery#

The era of the 24-month legacy rewrite is coming to an end. By leveraging Visual Reverse Engineering and metadata extraction, enterprise architects can finally see through the fog of technical debt. Faster knowledge graph construction isn't just a luxury; it is a necessity for organizations that need to move at the speed of the modern market without breaking the systems that keep them in business.

The future of legacy modernization is not found in the source code of the past, but in the visual metadata of the present.

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