Before the proliferation of AI, localization was often a nightmare. You had to hire translators, manage extensive JSON files for every language, and pray that your context didn’t get lost in translation. If you wanted to translate an entire website on the fly? Forget about it. That was a job for enterprise giants with bottomless budgets.

In this article, we will build Lingo.app from scratch. It is a stunning, production-ready web application that can scrape any website and instantly localize its content into 83+ languages. We will do this using the power of Next.js, Tailwind CSS, and the AI Website Content Localizer & Scraper Actor on the Apify platform.

We wrote this article for developers who want to ship global apps without the headache. Intermediate developers can likely speed through the setup, but we will explain every step clearly.

By the end of this article, you will have a fully functional app that manages scraping of any website, context-aware AI translation, and UI state—all in a scalable Next.js architecture.

Note: This article was originally published in Eunit.me.

If you are excited, then let’s dive in.

TL;DR

• Checkout the complete repository on GitHub: https://github.com/Eunit99/lingo.app 📖
• Checkout the deployed version of the Lingo.app live: https://lingo-app-azure.vercel.app/ 🚀

The Architecture

We are keeping it modern and simple:

• Frontend: Next.js 15 (App Router) for the framework.
• Styling: Tailwind CSS v4 for that premium, dark-mode aesthetic.
• Backend: A Next.js API Route acting as a secure proxy.
• Intelligence: The AI Website Content Localizer & Scraper Actor on Apify, which handles the headless browser scraping (Playwright) and AI translation (Lingo.dev).

File tree

Below is how the file structure of this app will be:

|   .gitignore
|   next-env.d.ts
|   eslint.config.mjs
|   next.config.ts
|   postcss.config.mjs
|   tsconfig.json
|   package.json
|   package-lock.json
|   env.example
|   .env.local
|   tsconfig.tsbuildinfo
|   README.md
|
+---app
|   |   page.tsx
|   |   globals.css
|   |   icon.png
|   |   layout.tsx
|   |
|   \---api
|       \---localize
|               route.ts
|
+---public
|       grid.svg
|
+---components
|       Features.tsx
|       Hero.tsx
|       LocalizationForm.tsx
|
+---lib
|       apify.ts

Prerequisites

To follow along, you will need:

• Node.js installed (v18 or higher).
• An Apify account. You can sign up for free.
• A text editor like VS Code.

Step 1: Initialize the project

First, we create a new Next.js application. We use the --no-src-dir flag to keep our project root clean and flat.

npx create-next-app@latest lingo.app --typescript --eslint --tailwind --no-src-dir --app

Navigate into your new directory and install the necessary dependencies, specifically the explicit React types to avoid TS errors and the Apify client.

cd lingo.app
npm install apify-client
npm install --save-dev @types/react @types/react-dom

Setting up the Global Design System

We want a premium feel right from the start. Open app/globals.css and replace the default styles with this modern Tailwind v4 setup. We are defining a deep slate color palette for a developer-focused dark mode.

@import "tailwindcss";

:root {
--background: #0f172a; /* Slate 900 */
--foreground: #f8fafc; /* Slate 50 */
}

body {
color: var(--foreground);
background: var(--background);
font-family: Arial, Helvetica, sans-serif;
overflow-x: hidden;
}

@layer components {
.btn-primary {
@apply px-6 py-3 bg-indigo-600 hover:bg-indigo-500 text-white rounded-lg font-medium transition-all duration-200 shadow-lg hover:shadow-indigo-500/30 active:scale-95 disabled:opacity-50 disabled:cursor-not-allowed;
}
.input-field {
@apply w-full bg-slate-800 border border-slate-700 rounded-lg px-4 py-3 text-white placeholder-slate-400 focus:outline-none focus:ring-2 focus:ring-indigo-500 focus:border-transparent transition-all;
}
.card {
@apply bg-slate-800/50 backdrop-blur-md border border-slate-700/50 rounded-xl p-6 shadow-xl;
}
}

Next, let’s configure the root layout to apply this background globally. Open app/layout.tsx. We are adding a subtle grid background pattern for texture.

import type { Metadata } from 'next';
import { Inter } from 'next/font/google';
import './globals.css';

const inter = Inter({ subsets: ['latin'] });

export const metadata: Metadata = {
title: 'Lingo.app - Globalize Your Web',
description: 'Instantly localize any website or text into 83+ languages using AI.',
};

export default function RootLayout({
children,
}: {
children: React.ReactNode;
}) {
return (
<html lang="en">
<body className={`${inter.className} min-h-screen bg-gradient-to-br from-slate-900 via-slate-900 to-indigo-950 selection:bg-indigo-500/30`}>
{/* Subtle Grid Background */}
<div className="fixed inset-0 bg-[url('/grid.svg')] bg-center [mask-image:linear-gradient(180deg,white,rgba(255,255,255,0))]" />

<main className="relative z-10 w-full max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
{/* Simple Header */}
<header className="flex justify-between items-center py-6 mb-12">
<div className="flex items-center space-x-2">
<span className="text-2xl font-bold bg-clip-text text-transparent bg-gradient-to-r from-white to-slate-400">lingo.app</span>
</div>
</header>
{children}
</main>
</body>
</html>
);
}

(Note: You will need a simple public/grid.svg file for the background. You can find free SVG patterns online or generate one.) See the one use for the lingo.app project on GitHub.

Step 2: Integrate the Apify Client

Now for the engine room. We need a way to communicate with the Apify platform securely.

The Client Wrapper (lib/apify.ts)

This client will enable communication with the AI Website Content Localizer & Scraper Actor on Apify.

Create a folder lib and a file apify.ts. This acts as our localized SDK. It initializes the client and defines the types for our input.

import { ApifyClient } from 'apify-client';

export interface LocalizerInput {
token: string;
lingoApiKey: string; // The secret key for Lingo.dev
mode: 'WEB' | 'TEXT';
targetLanguages: string[];
startUrls?: { url: string }[];
text?: string;
}

export const runLocalizerActor = async (params: LocalizerInput) => {
const { token, lingoApiKey, mode, targetLanguages, startUrls, text } = params;

const client = new ApifyClient({
token: token,
});

const input = {
mode,
lingoApiKey,
targetLanguages,
startUrls: mode === 'WEB' ? startUrls : undefined,
text: mode === 'TEXT' ? text : undefined,
};

try {
// Run the Actor and wait for it to finish
const run = await client.actor("eunit/ai-website-content-localizer-scraper").call(input);

if (!run) {
throw new Error("Actor run failed to start or return.");
}

// Fetch results from the default dataset
const { items } = await client.dataset(run.defaultDatasetId).listItems();
return items;
} catch (error) {
console.error("Apify Actor Run Error:", error);
throw error;
}
};

The API Proxy (app/api/localize/route.ts)

We cannot leak our Apify API Token to the client-side browser code. To solve this, we create an API Route that acts as a proxy. The client sends the URL/Text, and the server attaches the token and calls Apify.

Create app/api/localize/route.ts:

import { NextResponse } from 'next/server';
import { runLocalizerActor } from '@/lib/apify';

export async function POST(req: Request) {
try {
const body = await req.json();
const { url, text, mode, targetLanguages, lingoApiKey } = body;

const apifyToken = process.env.APIFY_API_TOKEN;

if (!apifyToken) {
return NextResponse.json({ error: "Server misconfigured: APIFY_API_TOKEN missing. Please set it in .env.local" }, { status: 500 });
}

const finalLingoKey = lingoApiKey || process.env.LINGO_API_KEY;
if (!finalLingoKey) {
return NextResponse.json({ error: "Lingo API Key is required. Please provide it in the form or set LINGO_API_KEY env." }, { status: 400 });
}

if (mode === 'TEXT' && text && text.length > 500) {
return NextResponse.json({ error: "Text content exceeds the 500 character limit." }, { status: 400 });
}

const startUrls = url ? [{ url }] : undefined;

// Default languages if not provided
const languages = targetLanguages && targetLanguages.length > 0 ? targetLanguages : ['es', 'fr', 'de'];

const items = await runLocalizerActor({
token: apifyToken,
lingoApiKey: finalLingoKey,
mode: mode as 'WEB' | 'TEXT',
targetLanguages: languages,
startUrls,
text
});

return NextResponse.json({ success: true, data: items || [] });
} catch (error: any) {
console.error("API Route Error:", error);
return NextResponse.json({ success: false, error: error.message || "Unknown error" }, { status: 500 });
}
}

To prevent usage abuse and manage API costs, we implemented a strict 500-character limit across both the frontend and backend.

In the user interface in components/LocalizationForm.tsx, we verified that the localizer input remains within bounds by adding a maxLength attribute and a real-time character counter (e.g., "0/500") that visually alerts the user as they type.

Crucially, we backed this up with server-side validation in app/api/localize/route.ts. Before sending any data to Apify, the API route checks the text length and immediately rejects requests exceeding 500 characters with a 400 Bad Request. This ensures the application remains secure and cost-efficient, even if the client-side restrictions are bypassed.

Step 3: Building the UI Components

Now let’s build the interactive frontend.

The Hero Component

In components/Hero.tsx, we create a bold introduction.

import React from 'react';

export default function Hero() {
return (
<div className="text-center max-w-3xl mx-auto space-y-6">
<div className="inline-flex items-center space-x-2 px-3 py-1 rounded-full bg-indigo-500/10 border border-indigo-500/20 text-indigo-400 text-sm font-medium mb-4">
<span>Powered by Lingo.dev & Apify</span>
</div>
<h1 className="text-5xl md:text-7xl font-extrabold tracking-tight text-white leading-tight">
Globalize your <span className="text-indigo-500">content</span> instantly.
</h1>
<p className="text-lg md:text-xl text-slate-400 max-w-2xl mx-auto">
Scrape any website and translate it into 83+ languages with context-aware AI.
The ultimate tool for shipping global apps fast.
</p>
</div>
);
}

The Main Logic: LocalizationForm

This is where the user interacts with the app. We need to handle:

1. Toggling between WEB (URL) and TEXT mode.
2. Selecting target languages.
3. Calling our API route.
4. Displaying the results in a tabbed interface.

Create components/LocalizationForm.tsx:

'use client';

import React, { useState } from 'react';

export default function LocalizationForm() {
const [mode, setMode] = useState<'WEB' | 'TEXT'>('WEB');
const [url, setUrl] = useState('');
const [textInput, setTextInput] = useState('');
const [selectedLangs, setSelectedLangs] = useState<string[]>(['es', 'fr', 'de']);
const [loading, setLoading] = useState(false);
const [results, setResults] = useState<any[]>([]);

// ... (Toggle logic helper functions)

const popularLangs = [
{ code: 'es', label: 'Spanish' },
{ code: 'fr', label: 'French' },
{ code: 'de', label: 'German' },
{ code: 'ja', label: 'Japanese' },
{ code: 'zh', label: 'Chinese' },
{ code: 'pt', label: 'Portuguese' },
{ code: 'it', label: 'Italian' },
{ code: 'ko', label: 'Korean' },
];

const toggleLang = (code: string) => {
if (selectedLangs.includes(code)) {
setSelectedLangs(selectedLangs.filter(c => c !== code));
} else {
setSelectedLangs([...selectedLangs, code]);
}
};

const handleSubmit = async (e: React.FormEvent) => {
e.preventDefault();
setLoading(true);
setResults([]);

try {
const res = await fetch('/api/localize', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
mode,
url: mode === 'WEB' ? url : undefined,
text: mode === 'TEXT' ? textInput : undefined,
targetLanguages: selectedLangs,
}),
});

const data = await res.json();
if (data.success) {
setResults(data.data);
}
} catch (err) {
console.error(err);
} finally {
setLoading(false);
}
};

return (
<div className="w-full max-w-4xl mx-auto space-y-8">
<div className="card">
{/* Mode Toggles */}
<div className="flex space-x-4 mb-6 border-b border-slate-700/50 pb-4">
<button onClick={() => setMode('WEB')} className={`... ${mode === 'WEB' ? 'text-indigo-400' : 'text-slate-400'}`}>
Web Scraper
</button>
<button onClick={() => setMode('TEXT')} className={`... ${mode === 'TEXT' ? 'text-indigo-400' : 'text-slate-400'}`}>
Text Localizer
</button>
</div>

<form onSubmit={handleSubmit} className="space-y-6">
{/* Input Fields specific to mode */}
{mode === 'WEB' ? (
<input type="url" value={url} onChange={(e) => setUrl(e.target.value)} className="input-field" placeholder="https://example.com" />
) : (
<div>
<div className="flex justify-between items-center mb-2">
<label className="block text-sm font-medium text-slate-300">Text Content</label>
<span className={`text-xs ${textInput.length >= 500 ? 'text-red-400' : 'text-slate-500'}`}>
{textInput.length}/500
</span>
</div>
<textarea
placeholder="Paste your text here to localize..."
className="input-field min-h-[150px]"
value={textInput}
onChange={(e) => setTextInput(e.target.value.slice(0, 500))}
maxLength={500}
required={mode === 'TEXT'}
/>
</div>
)}

{/* Language Selector Logic Here */}
{/* ... */}

<div className="flex justify-end">
<button type="submit" disabled={loading} className="btn-primary">
{loading ? 'Processing...' : 'Localize Content'}
</button>
</div>
</form>
</div>

{/* Results Rendering */}
{results.map((item, idx) => (
<ResultCard key={idx} item={item} />
))}
</div>
);
}

function ResultCard({ item }: { item: any }) {
// Shows original vs localized tabs
// ...
}

Step 4: Page Assembly

Finally, we string it all together in app/page.tsx.

import React from 'react';
import Hero from '@/components/Hero';
import LocalizationForm from '@/components/LocalizationForm';
import Features from '@/components/Features';

export default function Home() {
return (
<div className="flex flex-col space-y-24">
<div className="flex flex-col items-center justify-start space-y-12">
<Hero />
<LocalizationForm />
</div>
<Features />
</div>
);
}

Why the Apify Actor Integration Wins

You might be wondering: "Why can’t I just use fetch to get the HTML and send it to ChatGPT?"

You could try, but you would likely fail for three reasons. Integrating the AI Website Content Localizer & Scraper Actor via Apify solves these specific engineering headaches:

1. The "Context" Problem

Standard LLMs see text as a flat string. They don’t know that "Home" is a navigation button or that "Get Started" is a primary CTA. If you strip the HTML, you lose the context. If you keep the HTML, you confuse the model with thousands of lines of markup.

This AI Website Content Localizer & Scraper Actor is smart. It extracts the semantics of the page, identifies the translatable nodes, and sends them to Lingo.dev, which is specifically trained to understand UI and content context. The result? Buttons stay short, and articles sound natural.

2. The Scraping Battlefield

Modern websites are hostile. They use hydration (client-side rendering), anti-bot protections (Cloudflare, CAPTCHAs), and complex DOM structures.

• Without Apify: You have to manage a fleet of headless browsers, rotate proxies, handle retries, and parse dynamic JavaScript.
• With Apify: You make one API call. The Actor handles the headless browser infrastructure, proxy rotation, and Playwright execution for you.

3. Serverless Scalability

Translating a massive documentation site or a large e-commerce catalog requires significant computing power. If you run this logic in your own Next.js API route, you risk timeouts and server crashes.

By offloading this to Apify, you get a serverless queue system. You can submit up to 100 URLs at once, and the Apify platform scales up the necessary Actors to process them in parallel. Your Next.js app remains lightweight and responsive.

Step 5: Setup & Deploy

We are ready to launch. Create a .env.local file in your root folder:

APIFY_API_TOKEN=your_apify_token
LINGO_API_KEY=your_lingo_key

Get your APIFY_API_TOKEN by signing up at Apify. Apify gives you a free 5$ credit every month. This is more than enough to build your first app.

To get your LINGO_API_KEY, sign up at Lingo.dev. Lingo.dev gives you 10, 000 free words per month to try out new things.

Once set, it is time to run your app.

Running your App

Run your development server:

npm run dev

Open http://localhost:3000, and you will see your global localization app, ready to scrape and translate the web.

Wrapping Up

We didn’t just build a demo. We built a tool that addresses a real business problem: accessing global information. By using the AI Website Content Localizer & Scraper on Apify, we offloaded the most challenging aspects of scraping and AI context management, allowing us to focus on building a premium user experience.

If you are building global applications, you need tools that scale with you. Check out the AI Website Content Localizer & Scraper on Apify.

Happy building 🚀