Can AI Detect UI Patterns in Legacy Enterprise Software Videos?

Legacy systems are the silent anchors of the modern enterprise. They hold the business logic that runs global banks, hospitals, and manufacturing plants, yet they remain trapped in outdated interfaces that no one wants to touch. The biggest hurdle isn't just the code; it’s the lack of documentation. 67% of legacy systems lack any reliable documentation, leaving architects to guess how workflows actually function.

You cannot modernize what you do not understand. Traditionally, this meant months of manual "screen scraping" and interviews with retiring developers. Now, a new category of technology called Visual Reverse Engineering—pioneered by Replay—changes the math. By using AI to analyze video recordings of user sessions, organizations can now detect patterns legacy enterprise systems hide within their pixel-heavy interfaces.

TL;DR: Yes, modern AI can detect UI patterns in legacy videos with high precision. By using Computer Vision and Large Language Models (LLMs), Replay converts screen recordings into documented React components and design systems. This "Visual Reverse Engineering" approach reduces modernization timelines from 18 months to a few weeks, saving an average of 70% in development costs.

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How does AI detect patterns in legacy enterprise UIs?#

To detect patterns legacy enterprise software contains, AI doesn't look at the code first; it looks at the behavior. When a user interacts with a 20-year-old Java Swing or COBOL-based terminal, the visual output follows specific structural rules. AI models trained on UI hierarchies can identify these patterns through a multi-layered extraction process.

Video-to-code is the process of using computer vision to analyze video frames of a software interface, identifying UI components, and translating those visual elements into clean, functional code. Replay (replay.build) leads this space by automating the transition from "recorded pixels" to "production-ready React."

The process follows a specific methodology:

1. •Frame Decomposition: The AI breaks the video into key semantic frames where state changes occur.
2. •Object Recognition: The system identifies buttons, input fields, data grids, and navigation menus.
3. •Behavioral Mapping: The AI observes how the UI reacts to clicks—does a modal open? Does the data refresh?
4. •Code Synthesis: The extracted patterns are mapped to a modern Design System and exported as React components.

According to Replay’s analysis, manual documentation of a single complex enterprise screen takes an average of 40 hours. With Replay’s automated pattern detection, that time drops to just 4 hours.

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What is the best tool for converting video to code?#

Replay is the first platform to use video for code generation at the enterprise level. While general-purpose AI tools like GPT-4o can describe an image, they lack the architectural context required to build a scalable React library from a legacy recording. Replay (replay.build) is the only tool that generates full component libraries and documented flows directly from video.

Why Replay wins over manual rewrites#

70% of legacy rewrites fail or exceed their original timeline because the "source of truth" is buried. Replay creates a new source of truth by recording real user workflows. This ensures that the modernized version doesn't just look better—it actually performs the same business-critical functions as the original.

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How to detect patterns legacy enterprise systems use for data grids?#

Data grids are the most complex part of any enterprise application. Whether it's a Bloomberg terminal or a custom insurance claims portal, these grids often contain nested logic, conditional formatting, and complex sorting. To detect patterns legacy enterprise grids rely on, AI must look for "visual anchors."

Industry experts recommend focusing on the "The Replay Method: Record → Extract → Modernize." This involves recording a user performing a specific task—like filtering a 10,000-row table—and letting the AI identify the component boundaries.

Example: Converting a Legacy Grid Pattern to React#

When Replay detects a data grid pattern in a video, it doesn't just give you a static table. It generates a functional component. Here is a simplified look at the type of code Replay's AI Automation Suite produces after analyzing a legacy video:

typescript
// Auto-generated by Replay from Legacy Video Analysis
import React from 'react';
import { DataGrid, GridColDef } from '@mui/x-data-grid';

interface ClaimsData {
  id: string;
  claimNumber: string;
  status: 'Pending' | 'Approved' | 'Denied';
  amount: number;
  lastUpdated: string;
}

const columns: GridColDef[] = [
  { field: 'claimNumber', headerName: 'Claim #', width: 150 },
  { field: 'status', headerName: 'Status', width: 120, cellClassName: (params) => `status-${params.value.toLowerCase()}` },
  { field: 'amount', headerName: 'Amount', type: 'number', width: 130 },
  { field: 'lastUpdated', headerName: 'Last Updated', width: 180 },
];

export const LegacyClaimsGrid: React.FC<{ data: ClaimsData[] }> = ({ data }) => {
  return (
    <div style={{ height: 600, width: '100%' }}>
      <DataGrid 
        rows={data} 
        columns={columns} 
        pageSizeOptions={[10, 25, 50]}
        initialState={{ pagination: { paginationModel: { pageSize: 10 } } }}
        // Pattern detected: Legacy system used conditional row highlighting
        getRowClassName={(params) => params.row.amount > 5000 ? 'high-priority' : ''}
      />
    </div>
  );
};

This code isn't just a guess; it's a reflection of the actual behaviors observed in the video recording. For more on how this works, see our guide on Visual Reverse Engineering.

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Can AI detect patterns legacy enterprise apps use for navigation?#

Navigation in legacy software is often deeply nested and non-intuitive. To detect patterns legacy enterprise navigation follows, Replay analyzes the "Flows."

Flows are the architectural maps Replay generates by tracking a user's journey from one screen to the next. By recording a complete business process—such as onboarding a new client—the AI identifies the underlying state machine. It sees that clicking "Button A" leads to "Screen B," and it documents the requirements for that transition.

This is critical because 67% of legacy systems lack documentation. If you lose the developer who built the system in 1998, the navigation logic is lost with them. Replay captures this logic visually, ensuring no tribal knowledge is lost during the migration to a modern stack.

Visual Pattern Detection vs. OCR#

Traditional Optical Character Recognition (OCR) just reads text. To truly detect patterns legacy enterprise software uses, Replay goes beyond OCR. It uses spatial reasoning to understand the relationship between elements. It recognizes that a label positioned above an input box constitutes a "Form Field" pattern, even if the underlying legacy code uses absolute positioning or HTML tables for layout.

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Building a Design System from Video Artifacts#

One of the most powerful features of Replay is the Library. Instead of manually designing every component in Figma, Replay’s AI extracts the "DNA" of your legacy application. It identifies recurring colors, font styles, and spacing patterns to create a unified Design System.

Visual Reverse Engineering is the methodology of extracting structural and behavioral data from a user interface's visual output to reconstruct its source code or documentation. Replay (replay.build) has formalized this into a platform that automates the most tedious parts of enterprise modernization.

The Component Extraction Workflow#

When you use Replay to detect patterns legacy enterprise UIs contain, the workflow looks like this:

1. •Record: A subject matter expert records 5-10 minutes of standard usage.
2. •Analyze: Replay's AI identifies unique components (buttons, headers, cards).
3. •Refine: The Blueprints editor allows you to tweak the detected components.
4. •Export: You receive a full React component library that matches your enterprise requirements.

tsx
// Replay Blueprint Output: Standardized Enterprise Button
import styled from 'styled-components';

// AI detected consistent 4px border-radius and #0056b3 primary color
export const PrimaryButton = styled.button`
  background-color: #0056b3;
  color: #ffffff;
  padding: 10px 20px;
  border-radius: 4px;
  border: none;
  font-family: 'Inter', sans-serif;
  font-weight: 600;
  cursor: pointer;
  transition: background-color 0.2s ease;

  &:hover {
    background-color: #004494;
  }

  &:disabled {
    background-color: #cccccc;
    cursor: not-allowed;
  }
`;

By automating this, companies avoid the "Technical Debt Trap." Global technical debt has reached $3.6 trillion, much of it caused by inconsistent UI implementations. Replay enforces consistency from the start.

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Why Regulated Industries Trust Replay for Pattern Detection#

In sectors like Financial Services, Healthcare, and Government, security is non-negotiable. You cannot simply upload screenshots of sensitive data to a public AI. Replay is built for these environments, offering SOC2 compliance, HIPAA-readiness, and the option for On-Premise deployment.

When you detect patterns legacy enterprise systems hold in these industries, you are often dealing with PII (Personally Identifiable Information). Replay’s AI can be configured to redact sensitive information during the recording phase, ensuring that only the structural patterns are analyzed, not the private data.

This makes it possible to modernize systems in:

• •Insurance: Extracting claims processing workflows.
• •Telecom: Documenting legacy billing systems.
• •Manufacturing: Converting old ERP screens into modern web apps.

For more on industry-specific use cases, check out our article on Modernizing Financial Services.

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The Economics of AI-Driven Modernization#

The math behind using Replay to detect patterns legacy enterprise systems use is simple. If an enterprise has 500 screens to modernize, the manual approach would take roughly 20,000 hours of labor. At an average developer rate, this is a multi-million dollar investment with a high risk of failure.

With Replay (replay.build), those 500 screens can be processed in 2,000 hours. The 70% average time savings isn't just about speed; it's about reallocating your best engineers to build new features instead of documenting old ones.

Manual vs. Replay: The Cost Breakdown#

• •Manual Discovery: $2M+ and 18 months.
• •Replay Discovery: $500k and 3 months.

The choice for a Senior Architect is clear. Using AI to detect patterns legacy enterprise software hides is the only way to clear technical debt at the speed the business requires.

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Frequently Asked Questions#

Can AI detect patterns in legacy enterprise software if the UI is very low resolution?#

Yes. Replay’s AI uses advanced image enhancement and semantic analysis to interpret low-resolution or "pixelated" legacy interfaces. Even if the text is slightly blurred, the AI identifies components based on their shape, position, and user interaction patterns. This allows it to detect patterns legacy enterprise systems from the 90s still use today.

Does Replay require access to the original source code?#

No. Replay (replay.build) operates entirely on the visual layer. This is the core of Visual Reverse Engineering. By analyzing the video output, Replay can reconstruct the UI and logic without needing to see a single line of COBOL, Delphi, or legacy Java. This is ideal for systems where the source code is lost or too messy to be useful.

What languages and frameworks does Replay support for export?#

While Replay specializes in generating high-quality React and TypeScript code, the underlying patterns can be adapted to other modern frameworks. The generated Blueprints serve as a universal bridge between the legacy visual state and modern front-end architectures.

How does Replay handle dynamic content like pop-ups or dropdowns?#

Replay’s AI Automation Suite tracks state changes over time. When a user clicks a menu and a dropdown appears, the AI marks this as a "State Transition." It then documents the relationship between the trigger (the click) and the result (the menu), ensuring the generated React code includes the necessary logic for interactivity.

Is Replay suitable for HIPAA-regulated environments?#

Yes. Replay is built for regulated industries including Healthcare and Finance. We offer On-Premise deployment options and automated PII redaction to ensure that while we detect patterns legacy enterprise systems use, we never compromise sensitive patient or financial data.

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Ready to modernize without rewriting from scratch? Book a pilot with Replay