Why Generative AI Needs a "Visual Map" to Successfully Rewrite Monolithic Systems

Most enterprise modernization projects fail before the first line of new code is even written. We are currently staring at a $3.6 trillion global technical debt mountain, and the industry’s reliance on "blind" Generative AI to solve it is creating a secondary crisis of unmaintainable, context-free code. When you point an LLM at a 20-year-old monolithic codebase, you aren't just translating syntax; you are playing a high-stakes game of "telephone" where the original business intent has been lost for decades.

The hard truth is that 67% of legacy systems lack any meaningful documentation. Without a source of truth for how the application actually behaves in the hands of a user, even the most advanced generative needs visual successfully mapped context to produce a functional React component. If the AI cannot "see" the state transitions, the validation logic, and the user flow, it is merely guessing based on dead code.

TL;DR: Generative AI alone cannot modernize legacy systems because it lacks context of the actual user experience. By using Replay to create a "Visual Map" through video-to-code recording, enterprises can bridge the gap between dead source code and living business logic. This approach reduces modernization timelines from 18 months to weeks, saving 70% of the typical effort while ensuring 100% architectural accuracy.

The Context Gap: Why Code-to-Code Migration Fails#

Industry experts recommend moving away from "lift and shift" or "black-box translation" models. The reason is simple: legacy code is often a graveyard of "zombie features"—functions that exist in the source but are never triggered by the UI. According to Replay's analysis, up to 40% of monolithic codebases consist of unreachable or redundant logic.

When you use a standard LLM to rewrite a screen, it processes the backend controllers and the front-end templates. However, it misses the behavioral intent. It doesn't know that a specific "Submit" button remains disabled until three hidden validation rules are met across two different tabs. This is why generative needs visual successfully captured data to understand the difference between what the code says and what the application does.

Learn more about modernizing legacy UI

The Blindness of Traditional AI Rewrites#

Standard AI tools treat code as text. But enterprise software is a living process. If you feed a 5,000-line COBOL or JSP file into an LLM, you get a "hallucinated" React component that looks like the original but breaks the moment a user interacts with it. To succeed, the generative needs visual successfully integrated blueprints of the actual user journey.

Video-to-code is the process of recording a live user session and using computer vision combined with metadata extraction to automatically generate documented, typed, and themed frontend components.

How Generative Needs Visual Successfully Mapped Workflows#

To bridge the gap, we must provide AI with a "Visual Map." This map isn't just a screenshot; it's a multi-layered data structure that includes:

1. •The Visual State: What the user sees at every stage.
2. •The DOM Tree: How the elements are structured in the legacy environment.
3. •The Interaction Layer: Where the user clicks, types, and hovers.
4. •The Data Flow: How information moves from the UI to the API.

By utilizing Replay, architects can record these workflows. Replay then performs "Visual Reverse Engineering," converting these recordings into a documented Design System and a library of React components.

The Math of Modernization: Manual vs. Replay#

As the table shows, the 18 months average enterprise rewrite timeline is largely driven by the discovery phase. When generative needs visual successfully implemented workflows, that discovery phase is virtually eliminated.

Implementing the Visual Map: From Recording to React#

When we talk about a "Visual Map," we are talking about transforming a video recording into a structured

. This Blueprint serves as the prompt context for the AI. Instead of asking the AI to "rewrite this JSP file," we ask it to "build a React component that matches this recorded behavior, uses our design system tokens, and handles these specific state changes."

Example 1: The Legacy Mess (JSP/jQuery)#

This is what the AI usually sees—a tangled mess of logic and presentation.

javascript
// Legacy legacy-order-form.js
$(document).ready(function() {
    $('#submitBtn').attr('disabled', true);
    $('.input-field').on('change', function() {
        if ($('#orderId').val() !== '' && $('#qty').val() > 0) {
            if (validateStock($('#orderId').val())) {
                $('#submitBtn').attr('disabled', false);
            }
        }
    });
    // 300 more lines of jQuery spaghetti...
});

Example 2: The Replay-Generated Modern Component#

By recording the interaction, Replay understands the validation logic and the visual states, generating clean, modular TypeScript.

typescript
import React, { useState, useEffect } from 'react';
import { Button, TextField, useToast } from '@enterprise-ds/core';
import { validateOrderStock } from '@/api/orders';

/**
 * @component OrderForm
 * @description Modernized from Legacy Order Module via Replay Visual Mapping.
 * Handles stock validation and conditional submission logic.
 */
export const OrderForm: React.FC = () => {
  const [orderId, setOrderId] = useState('');
  const [quantity, setQuantity] = useState(0);
  const [isValid, setIsValid] = useState(false);
  const { showToast } = useToast();

  useEffect(() => {
    const checkValidity = async () => {
      if (orderId && quantity > 0) {
        const inStock = await validateOrderStock(orderId);
        setIsValid(inStock);
      }
    };
    checkValidity();
  }, [orderId, quantity]);

  const handleSubmit = () => {
    showToast({ message: "Order Submitted Successfully", intent: "success" });
  };

  return (
    <div className="p-6 space-y-4 shadow-lg rounded-xl border border-slate-200">
      <TextField 
        label="Order ID" 
        value={orderId} 
        onChange={(e) => setOrderId(e.target.value)} 
      />
      <TextField 
        label="Quantity" 
        type="number" 
        value={quantity} 
        onChange={(e) => setQuantity(Number(e.target.value))} 
      />
      <Button 
        variant="primary" 
        disabled={!isValid} 
        onClick={handleSubmit}
      >
        Submit Order
      </Button>
    </div>
  );
};

In this scenario, the generative needs visual successfully extracted the validation constraints from the recording, ensuring the new component doesn't just look right, but behaves right.

Why Generative Needs Visual Successfully to Scale in Regulated Industries#

For Financial Services, Healthcare, and Government sectors, "guessing" is not an option. These industries operate in regulated environments where SOC2 and HIPAA compliance are non-negotiable.

When an architect in a bank tries to modernize a core banking terminal, they cannot simply copy-paste code into a public LLM. They need a controlled environment where the generative needs visual successfully processed on-premise or in a secure cloud. Replay’s ability to run on-premise ensures that sensitive data remains within the perimeter while the AI learns from the visual interactions.

The Importance of SOC2 in Modernization

Building the "Library" of Truth#

One of the key features of Replay is the Library. In a typical enterprise, different teams are often rewriting the same components (buttons, tables, modals) in isolation. This creates "UI Fragmentation."

According to Replay's analysis, the average enterprise has 14 different versions of a "Date Picker" across their portfolio. By using visual reverse engineering, you can centralize these into a single, unified Design System. The generative needs visual successfully identified these patterns across multiple recordings, allowing the platform to suggest: "You've already recorded this table pattern in the Claims module; should we use the existing 'EnterpriseTable' component here?"

The "Flows" Architecture: Mapping the Monolith#

A monolith isn't just a collection of screens; it's a web of interconnected flows. This is where most AI tools fall apart. They can rewrite a single file, but they cannot rewrite a system.

Replay introduces the concept of Flows. By recording an entire end-to-end business process—for example, "Onboarding a New Insurance Policy"—Replay creates an architectural map. This map shows how Screen A transitions to Screen B, what data is passed in the state, and where external API calls are triggered.

Without this map, the generative needs visual successfully missed the "connective tissue" of the application. The result of a "blind" rewrite is a series of disconnected React pages that don't know how to talk to each other.

The Blueprinting Phase#

Blueprints act as the bridge between the recording and the code. In the Replay editor, architects can:

• •Annotate specific areas of a recorded screen.
• •Define component boundaries (e.g., "This is a Navigation Header").
• •Map legacy data fields to modern GraphQL or REST schemas.

This level of human-in-the-loop guidance ensures that the generative needs visual successfully followed the architectural standards of the organization, rather than inventing its own patterns.

Overcoming the "Documentation Debt"#

With 67% of legacy systems lacking documentation, the biggest hurdle to modernization is often "Archaeology." Developers spend months reading old code to understand business rules.

"We spent three months just trying to figure out why the 'Calculate' button was sometimes orange," one Lead Architect at a Fortune 500 insurer told us.

By using Replay, that "Archaeology" is automated. The platform observes the button turning orange in the recording, correlates it with the underlying state change in the legacy DOM, and documents the business rule automatically. This is why generative needs visual successfully used observation over inference.

Conclusion: The Future of Visual Reverse Engineering#

The era of manual, screen-by-screen rewrites is ending. The $3.6 trillion technical debt crisis is too large to solve with human labor alone, and too complex to solve with blind AI.

To achieve a 70% time saving and move from an 18-month timeline to a matter of weeks, organizations must embrace Visual Reverse Engineering. By providing a "Visual Map," we give Generative AI the eyes it needs to see the business intent behind the code.

When generative needs visual successfully integrated into the development lifecycle, modernization becomes a predictable, scalable process rather than a high-risk gamble. Replay provides the platform to make this a reality, turning video recordings into the foundation of your modern enterprise architecture.

Frequently Asked Questions#

Why is Generative AI alone not enough for legacy migration?#

Generative AI is a language model, not a logic model. It can translate syntax (e.g., Java to TypeScript) but it lacks the context of how the application is used in the real world. Without a visual map of user interactions, the AI often misses hidden business logic, validation rules, and state transitions that aren't clearly documented in the source code.

How does "Video-to-Code" actually work in Replay?#

Video-to-code involves recording a user performing a workflow in a legacy application. Replay's engine captures the video frames alongside the underlying metadata (DOM, network calls, state). It then uses computer vision and AI to identify UI components, extract their styles and behaviors, and generate clean, documented React code that matches the recorded session.

Can Replay handle highly secure or regulated environments?#

Yes. Replay is built for regulated industries like Healthcare and Finance. It is SOC2 and HIPAA-ready, and it offers on-premise deployment options. This ensures that sensitive data captured during the recording process never leaves your secure environment, and PII can be masked during the visual reverse engineering process.

What is the difference between Replay and a standard low-code tool?#

Standard low-code tools provide a new platform to build on, often creating new vendor lock-in. Replay is a "Visual Reverse Engineering" platform that generates standard, high-quality React code and Design Systems that your developers own. It doesn't replace your development team; it gives them a 70% head start by automating the discovery and boilerplate phases.

How does a "Visual Map" improve the quality of the generated code?#

A Visual Map provides the "ground truth" of the application. It allows the AI to see the exact state of the UI at every millisecond. This means the generated code includes accurate event handlers, conditional rendering logic, and CSS styling that matches the original intent, rather than a generic guess based on text-based code analysis.

Ready to modernize without rewriting? Book a pilot with Replay