Legacy CRM Field Mapping: Using Visual Data to Drive React Prop Types

Legacy CRMs are where data goes to die—or at least where it becomes unreadable to modern frontends. If you are currently staring at a 20-year-old Oracle or SAP instance trying to figure out if

represents a customer’s middle name or their credit limit, you are not alone. The $3.6 trillion global technical debt isn't just a number; it’s a daily reality for enterprise architects tasked with "modernizing" systems that haven't seen a documentation update since the early 2000s.

The biggest bottleneck in any modernization project isn't writing the new React code—it’s the discovery phase. Legacy field mapping using manual spreadsheets is a recipe for project failure. According to Replay’s analysis, 67% of legacy systems lack any form of accurate documentation, forcing developers to play digital archaeology before they can write a single line of TypeScript.

TL;DR: Manual field mapping for legacy CRM migrations is slow and error-prone, contributing to the 70% failure rate of enterprise rewrites. By using Replay, architects can record user workflows to visually extract data requirements, automatically generating React prop types and component libraries. This shifts the timeline from 18 months to weeks, reducing manual effort from 40 hours per screen to just 4 hours.

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Why Legacy Field Mapping Using Manual Documentation Always Fails#

In a typical enterprise environment, the process of moving from a legacy CRM (like Siebel or a custom Delphi app) to a modern React-based frontend follows a predictable, painful path. A business analyst records a screen, a developer tries to find the corresponding database table, and a QA engineer eventually discovers that the "Address Line 2" field is actually being used to store internal discount codes.

The industry standard for a full-scale enterprise rewrite is roughly 18 months. During this time, the business requirements often change, and the legacy system continues to evolve, making the initial mapping obsolete before the first sprint ends.

Visual Reverse Engineering is the process of capturing the runtime behavior and visual state of a legacy application to automatically generate technical specifications, code, and design assets.

Industry experts recommend moving away from "static mapping" (looking at DB schemas) and toward "behavioral mapping" (looking at how data is actually rendered). This is where Replay changes the game. Instead of guessing what a field does, you record a user performing a task. Replay analyzes the visual output and the underlying DOM/data structure to determine exactly what props your new React components need.

The Cost of Manual Mapping vs. Automated Visual Mapping#

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Automating Legacy Field Mapping Using Visual Reverse Engineering#

When we talk about legacy field mapping using visual data, we are moving the source of truth from the database to the User Interface (UI). In many legacy systems, the database is a "junk drawer." However, the UI is a filter; it only shows what the user actually needs to see.

By recording a session in a legacy CRM, Replay identifies the data patterns. For example, if a field consistently displays a currency symbol followed by a number, the AI Automation Suite in Replay flags this as a

type. If a dropdown menu contains a list of US states, it generates a TypeScript .

Modernizing Legacy UI requires more than just a fresh coat of paint; it requires a deep understanding of the data contracts that have kept the business running for decades.

From Video to TypeScript: The Workflow#

1. •Record: A subject matter expert (SME) records a standard workflow (e.g., "Creating a New Lead") in the legacy CRM.
2. •Analyze: Replay’s engine breaks down the video frame-by-frame, identifying input fields, labels, and data relationships.
3. •Extract: The platform extracts the "Blueprints"—the architectural map of how data flows through that specific screen.
4. •Generate: Replay produces documented React components with prop types that mirror the legacy data requirements but follow modern best practices.

Video-to-code is the process of converting visual recordings of software interactions into functional, production-ready source code, typically involving the extraction of UI patterns and data structures.

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Implementation: Mapping Visual States to TypeScript Interfaces#

Let’s look at a practical example. Imagine a legacy CRM screen that displays customer information. The underlying data structure is a flat, unorganized JSON object returned from a COBOL-based mainframe.

Step 1: The Legacy Data Mess#

The legacy system might provide data that looks like this:

typescript
// The "Junk Drawer" data from the legacy API
interface LegacyCrmResponse {
  CUST_ID_01: string;
  VAL_STR_ADDR: string;
  DT_ENTRY: number; // Unix timestamp? Or just a random number?
  FLAG_01: "Y" | "N";
  FIELD_42: string; // Turns out this is "Credit Score"
}

Step 2: Visual Mapping with Replay#

When you record the UI, Replay sees that

is rendered under a label titled "Credit Rating" and is styled with red text if the value is below 600. It also sees that is rendered as a "Premium Member" checkbox.

Replay then generates a clean, documented React component. Instead of you manually writing the mapping logic, the platform uses its "Flows" feature to map these visual elements to modern prop types.

Step 3: The Generated React Component#

Here is what the output looks like after legacy field mapping using Replay’s visual extraction:

tsx
import React from 'react';

/**
 * @component CustomerProfile
 * @description Modernized profile component mapped from Legacy CRM Lead View.
 * Extracted via Replay Visual Reverse Engineering.
 */

interface CustomerProfileProps {
  /** Mapped from CUST_ID_01 */
  id: string;
  /** Mapped from VAL_STR_ADDR */
  billingAddress: string;
  /** Mapped from DT_ENTRY - Converted to JS Date */
  enrollmentDate: Date;
  /** Mapped from FLAG_01 - Boolean conversion */
  isPremiumMember: boolean;
  /** Mapped from FIELD_42 - Identified as Credit Score */
  creditScore: number;
}

export const CustomerProfile: React.FC<CustomerProfileProps> = ({
  id,
  billingAddress,
  enrollmentDate,
  isPremiumMember,
  creditScore
}) => {
  const isHighRisk = creditScore < 600;

  return (
    <div className="p-6 bg-white rounded-lg shadow-md border-l-4 border-blue-500">
      <h2 className="text-xl font-bold">Customer: {id}</h2>
      <p className="text-gray-600">{billingAddress}</p>
      <div className="mt-4">
        <span className={`px-2 py-1 rounded ${isHighRisk ? 'bg-red-100 text-red-800' : 'bg-green-100 text-green-800'}`}>
          Credit Rating: {creditScore}
        </span>
        {isPremiumMember && (
          <span className="ml-2 px-2 py-1 bg-gold-100 text-gold-800 border border-gold-500">
            Premium Member
          </span>
        )}
      </div>
      <p className="text-sm mt-2 text-gray-400">
        Member since: {enrollmentDate.toLocaleDateString()}
      </p>
    </div>
  );
};

By focusing on the visual output, we’ve transformed a cryptic legacy record into a self-documenting React component. This approach saves an average of 36 hours per screen compared to manual reverse engineering.

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Bridging the Gap Between Design Systems and Legacy Data#

One of the hardest parts of legacy field mapping using traditional methods is maintaining design consistency. When you use Replay, you aren't just getting code; you are building a Design System based on actual usage.

Replay's "Library" feature acts as a repository for these extracted components. As you record more of the legacy CRM, Replay identifies repeating patterns. If the "Save" button in the legacy app always triggers a specific validation modal, Replay recognizes this as a reusable component pattern.

According to Replay’s analysis, enterprise teams that use a visual-first approach to component extraction reduce their technical debt by 45% in the first year. This is because the generated code is standardized, following the same prop-type conventions across the entire application, rather than being subject to the varying styles of different developers.

Handling Complex Mappings: Nested Objects and Validation#

Legacy CRMs often use "flat" data structures for everything. A modern React application, however, thrives on nested objects and clean separation of concerns.

When performing legacy field mapping using Replay, the platform’s AI Automation Suite can infer relationships. If three fields always appear together in a "Contact" section of the legacy screen, Replay can group them into a single

prop object.

typescript
// Replay-generated nested prop mapping
interface ContactInfo {
  firstName: string;
  lastName: string;
  email: string;
  phone: string;
}

interface ModernLeadsProps {
  contact: ContactInfo;
  leadSource: 'Web' | 'Referral' | 'Cold Call'; // Enum inferred from legacy dropdown values
  priority: number;
}

This level of inference is impossible with simple script-based migrations. It requires a visual understanding of how the data is grouped and presented to the end user.

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Security and Compliance in Legacy Modernization#

For industries like Financial Services, Healthcare, and Government, the process of legacy field mapping using third-party tools can be a security concern. This is why Replay is built for regulated environments.

• •SOC2 & HIPAA Ready: Data extraction processes comply with the highest security standards.
• •On-Premise Availability: For organizations that cannot let their legacy data leave their firewall, Replay offers on-premise deployments.
• •Data Masking: During the recording phase, sensitive PII (Personally Identifiable Information) can be masked, ensuring that the visual reverse engineering process captures the structure of the data without capturing the sensitive values.

Industry experts recommend that any modernization tool used in a regulated environment must provide a clear audit trail of how data mappings were derived. Replay provides this by linking every generated component back to the original video recording (the "Flow"), creating a transparent link between the legacy "as-is" and the modern "to-be."

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The Strategic Advantage of Visual Reverse Engineering#

The $3.6 trillion technical debt problem isn't going away by hiring more developers to write more code. It goes away by automating the most tedious parts of the development lifecycle: discovery and mapping.

By utilizing Replay, enterprise architects can reclaim their timelines. Instead of an 18-month "big bang" rewrite that has a 70% chance of failure, teams can adopt an iterative approach. They can record a single module, map the fields visually, generate the React components, and deploy—all within a single sprint.

Ready to modernize without rewriting? Book a pilot with Replay and see how visual data can accelerate your React migration.

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Frequently Asked Questions#

How does Replay handle legacy systems with no API?#

Replay doesn't require an API to function. Because it uses Visual Reverse Engineering, it extracts data structures and UI patterns directly from the visual output and DOM of the legacy application. This makes it ideal for "black box" systems where the source code or database access is restricted or lost.

Can Replay generate TypeScript types for nested legacy data?#

Yes. Replay’s AI Automation Suite analyzes the spatial relationship of fields on the screen. If it detects a group of fields (like an address block), it can automatically suggest a nested TypeScript interface, turning a flat legacy structure into a modern, hierarchical one.

Does legacy field mapping using Replay work with desktop applications?#

Replay is primarily optimized for web-based legacy systems (like older versions of Salesforce, SAP, or custom internal portals). However, for desktop applications, Replay can analyze video recordings to generate design specs and component outlines, though the direct "video-to-code" extraction is most powerful in browser-based environments.

What is the average time savings when using Replay?#

According to Replay's analysis, the average time savings is 70%. Specifically, the manual process of documenting, mapping, and coding a single complex legacy screen typically takes 40 hours. With Replay, this is reduced to approximately 4 hours, including the recording and the AI-assisted code generation.

How does Replay ensure the generated React code is high quality?#

Replay doesn't just "scrape" the UI. It generates code based on your organization's specific "Blueprints." You can define your preferred styling (e.g., Tailwind, Material UI) and coding standards. The AI then ensures that every generated component adheres to these rules, creating a consistent, high-quality component library.

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Ready to modernize without rewriting? Book a pilot with Replay