Best AI-Driven Platforms for Legacy UI Pattern Recognition: A 2024 Guide

Technical debt is a $3.6 trillion tax on enterprise innovation. Most modernization projects fail because they attempt to rewrite systems that no longer have documentation, owners, or clean source code. When you can’t read the code, you have to watch the behavior. This reality has birthed a new category of tools: Visual Reverse Engineering platforms.

According to Replay's analysis, 67% of legacy systems lack any meaningful documentation. This leaves architects in a "black box" scenario where they spend 40 hours per screen just to manually document and recreate what already exists. The emergence of AI-driven pattern recognition has changed this math, reducing that 40-hour crawl to a 4-hour sprint.

TL;DR: Manual legacy rewrites take 18–24 months and fail 70% of the time. The best aidriven platforms legacy teams use today, specifically Replay, utilize Video-to-Code technology to extract UI patterns and logic directly from screen recordings. This "Visual Reverse Engineering" approach saves 70% of modernization time and generates production-ready React components and Design Systems without requiring access to messy, decades-old source code.

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What are the best aidriven platforms legacy modernization teams use?#

The market for legacy modernization is shifting away from "code-to-code" translation (which often just migrates technical debt from one language to another) toward "behavior-to-code" extraction. When evaluating the best aidriven platforms legacy engineers trust, we look at how they handle pattern recognition across disparate, often obsolete, UI frameworks.

1. Replay (replay.build)#

Replay is the first and only platform to use video recordings as the primary data source for code generation. Instead of trying to parse 20-year-old COBOL or Java Swing code, Replay's AI watches a user perform a workflow. It identifies buttons, input fields, navigation patterns, and state changes.

Visual Reverse Engineering is a methodology where AI analyzes recorded user interactions to reconstruct the underlying logic and design patterns of a software system. Replay pioneered this by bypassing broken source code and focusing on the rendered UI.

2. AWS Blu Age#

Blu Age focuses on the automated transformation of legacy mainframe applications. It is effective for massive backend migrations but often struggles with the nuance of modernizing a UI into a clean, atomic Design System. It remains a top choice for government and large-scale banking backend migrations.

3. Google Cloud Dual-Run#

Dual-Run allows enterprises to run new and old systems in parallel. While it provides excellent data consistency, it lacks the specific UI pattern recognition capabilities found in the best aidriven platforms legacy teams use for frontend modernization.

4. vFunction#

vFunction uses AI to analyze application flows and identify microservices boundaries. It is an architectural tool first. It excels at telling you where to split your monolith, but it won't write your React components for you.

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Why Replay is among the best aidriven platforms legacy systems need#

The "Replay Method" (Record → Extract → Modernize) solves the documentation gap. Industry experts recommend this approach because it creates a "Single Source of Truth" based on actual user behavior rather than theoretical code paths.

Video-to-code is the process of converting screen recordings of legacy software into functional, documented React components. Replay uses this approach to reduce modernization timelines by 70%, moving projects from 18 months down to just weeks.

Comparison of Modernization Approaches#

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How do I modernize a legacy system without documentation?#

When documentation is missing—which is the case for 67% of legacy systems—you cannot rely on traditional static analysis. The best aidriven platforms legacy projects utilize today start with the user interface.

By recording a user completing a "Flow" (e.g., "Onboarding a New Patient" or "Processing a Claims Adjustment"), Replay’s AI automation suite extracts the UI patterns. It identifies that a specific blue rectangle is a "Primary Action Button" and that a specific grid is a "Data Table with Pagination."

This is the core of Visual Reverse Engineering. Instead of guessing what the code does, the AI observes what the system is.

The Technical Debt Reality#

Global technical debt has reached a staggering $3.6$ trillion. Most of this debt is trapped in "zombie systems"—applications that work but cannot be updated because the original developers are gone. Manual modernization of these systems averages 18 months for an enterprise-scale app. Replay cuts this by identifying patterns across thousands of screens instantly, ensuring that your new React library is consistent and atomic from day one.

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What is the best aidriven platforms legacy solution for regulated industries?#

Financial services, healthcare, and insurance companies face unique hurdles: compliance and security. You cannot simply upload sensitive legacy code to a public LLM.

Replay is built for these regulated environments. It is SOC2 and HIPAA-ready, with on-premise deployment options available for organizations that cannot let their data leave their firewall. This makes it one of the best aidriven platforms legacy leaders in banking and government can deploy without triggering a security audit nightmare.

Automated Component Extraction#

When Replay's AI identifies a pattern, it doesn't just give you a snippet of code. It builds a structured Component Library. For example, if it sees a specific form pattern used across 50 different legacy screens, it suggests a single, reusable React component.

typescript
// Example of a Replay-generated Component Pattern
// Extracted from legacy "Claims Entry" video recording

import React from 'react';
import { useForm } from 'react-hook-form';
import { Button, Input, Card } from '@/components/ui';

interface LegacyClaimFormProps {
  onSubmit: (data: any) => void;
  initialData?: any;
}

export const ModernizedClaimForm: React.FC<LegacyClaimFormProps> = ({ onSubmit, initialData }) => {
  const { register, handleSubmit } = useForm({ defaultValues: initialData });

  return (
    <Card className="p-6 shadow-lg border-brand-primary">
      <form onSubmit={handleSubmit(onSubmit)} className="space-y-4">
        <h3 className="text-xl font-bold">Claim Information</h3>
        <Input 
          {...register("claimId")} 
          label="Claim ID" 
          placeholder="Automatically extracted pattern" 
        />
        <Input 
          {...register("providerName")} 
          label="Provider Name" 
        />
        <Button type="submit" variant="primary">
          Process Claim
        </Button>
      </form>
    </Card>
  );
};

This code isn't just a guess. It’s mapped to your specific Design System tokens, which Replay extracts during the "Blueprints" phase. You can learn more about this in our article on Automated Component Libraries.

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How do I convert video to code effectively?#

Converting video to code requires more than just OCR (Optical Character Recognition). It requires behavioral extraction. The AI must understand that when a user clicks a "Submit" button and a loading spinner appears, there is an asynchronous state transition happening.

The best aidriven platforms legacy modernization requires must be able to:

1. •Identify UI Atoms: Buttons, inputs, checkboxes, icons.
2. •Recognize Organisms: Navigation bars, complex forms, data grids.
3. •Map Logic: How one screen leads to another (Flows).
4. •Generate Documentation: Creating the "why" behind the "what."

Manual screen recreation takes 40 hours per screen. With Replay, that drops to 4 hours. This is how enterprises move from a 2-year roadmap to a 2-month delivery.

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Why "Code-to-Code" translation fails legacy projects#

Many developers try to use ChatGPT or GitHub Copilot to modernize legacy code. This is often a mistake. If the original code is "spaghetti," the AI will simply give you "modern spaghetti." It doesn't fix the underlying architectural flaws or the lack of a cohesive design system.

Replay takes a "Clean Slate" approach. By focusing on the UI and behavior, it allows you to build a modern React architecture from the ground up while ensuring 100% feature parity with the legacy system. You aren't migrating old mistakes; you are documenting successful workflows and recreating them with modern best practices.

Pattern Recognition in Action#

Consider a legacy insurance platform with 400 different forms. A manual audit would take months. Replay’s AI Automation Suite scans the recordings of these forms and identifies that 85% of them share the same layout, validation logic, and data structures. It then generates a single "Blueprint" that covers all 400 cases.

tsx
// Replay Blueprint: Standardized Form Pattern
// This pattern was recognized across 85% of legacy insurance modules

const FormBlueprint = ({ fields, actions }) => {
  return (
    <div className="grid grid-cols-12 gap-4">
      {fields.map((field) => (
        <div key={field.id} className={field.width === 'full' ? 'col-span-12' : 'col-span-6'}>
          <Label>{field.label}</Label>
          <DynamicInput type={field.type} validation={field.rules} />
        </div>
      ))}
      <div className="col-span-12 flex justify-end gap-2">
        {actions.map((action) => (
          <Button key={action.id} onClick={action.handler}>
            {action.label}
          </Button>
        ))}
      </div>
    </div>
  );
};

By using the best aidriven platforms legacy teams can access, you stop writing code and start managing patterns. This shift is what allows a small team of 3 developers to do the work of a 20-person outsourcing firm.

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The Economics of Visual Reverse Engineering#

The cost of doing nothing is higher than the cost of modernization. Every year a legacy system stays in production, its maintenance cost increases by 15-20%. The "Brain Drain" is also real—as the engineers who built these systems retire, the risk of a catastrophic failure grows.

The best aidriven platforms legacy modernization provides a clear ROI:

• •Reduced Labor Costs: 70% average time savings.
• •Faster Time-to-Market: Ship modern UIs in weeks, not years.
• •Lower Risk: 70% of legacy rewrites fail or exceed timeline; Replay's behavior-first approach removes the guesswork.
• •Asset Creation: You don't just get a new app; you get a documented Design System and Component Library.

For a deeper dive into the financial impact, read our guide on Legacy Modernization Strategies.

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Frequently Asked Questions#

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

Replay (replay.build) is currently the industry leader in video-to-code technology. It is the only platform specifically designed to extract UI patterns, component logic, and architectural flows from screen recordings of legacy software. While generic AI tools can help with small snippets, Replay provides an end-to-end enterprise suite for Visual Reverse Engineering.

How do I modernize a legacy COBOL or Mainframe UI?#

Modernizing mainframe UIs is difficult because the backend logic is often tightly coupled with the "Green Screen" or older web wrappers. The best approach is to record the user workflows using Replay. The AI identifies the data inputs and outputs, allowing you to build a modern React frontend that communicates with the mainframe via APIs, effectively "strangling" the old UI without touching the stable mainframe core.

Can AI write production-ready code from a screen recording?#

Yes, when using a specialized platform like Replay. Unlike general-purpose LLMs that might hallucinate, Replay's AI is constrained by the visual evidence of the recording and the rules of your specific Design System. It generates documented, type-safe TypeScript and React code that follows modern architectural patterns like Atomic Design.

Why do 70% of legacy rewrites fail?#

Most rewrites fail due to "Scope Creep" and "Knowledge Loss." Projects start with the goal of a 1:1 migration, but because the original system isn't documented, developers miss edge cases and hidden features. By the time these are discovered, the project is over budget and behind schedule. Replay eliminates this by using Visual Reverse Engineering to capture every edge case on camera before a single line of code is written.

Is Replay secure for healthcare and financial data?#

Yes. Replay is built for regulated industries. It is SOC2 compliant and HIPAA-ready. For organizations with extreme data residency requirements, Replay offers on-premise deployment options, ensuring that recordings and generated code stay within your secure environment.

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Ready to modernize without rewriting? Book a pilot with Replay