🌐 Breaking Language Barriers: Building a Real-Time Multilingual Chat App with Next.js & Lingo.Dev

Communication is the foundation of every online community. But the moment people from different countries join the same space, one invisible problem shows up fast: language.

You might be comfortable chatting in English, while someone else prefers Hindi, Spanish, or Japanese. Most chat apps assume everyone will “figure it out” — but in reality, that often leads to confusion, hesitation, or people staying silent.

I wanted to fix that.

So I built FlowTalk, a real-time multilingual chat application where users can write in their own language and instantly read messages in theirs — without breaking the flow of conversation.

In this article, I’ll walk through how I built it, the unexpected translation problems I ran into, and how I used Lingo.dev to solve them in a clean, scalable way.

---

🚀 The Tech Stack

I kept the stack modern but practical:

• Frontend: Next.js 16 (App Router) + React 19

• Styling: Tailwind CSS v4

• Backend: Next.js API routes + MongoDB

• Real-time updates: Pusher / polling

• Translation layer: Lingo.dev

Nothing exotic — just tools that work well together.

---

🏗️ High-Level Architecture

The core challenge wasn’t how to translate text.

It was how to translate text without slowing down a real-time chat.

Here’s the flow I ended up with:

1. A user sends a message (for example, “Hello everyone”)

2. The server receives the message and detects the source language

3. A Translation Engine checks if translations already exist (cache first)

4. If not cached, it calls Lingo.dev to generate translations

5. The message is broadcast to all connected clients

6. Each client displays the message in the user’s preferred language

Only one message is sent — translation is treated as metadata, not a new chat message.

---

🧠 The Real Translation Problems (That No One Mentions)

This is where things got interesting.

Once real users started testing the app, I discovered problems that don’t show up in simple demos.

---

1. Script ≠ Language (Hinglish Is Real)

One of the most common messages I saw was something like:

muje aapki help chahiye

This is Hindi, but written using English letters (often called Hinglish).

Most systems either:

• Treat it as English (wrong), or

• Don’t translate it at all (also wrong)

So FlowTalk detects language intent, not just the alphabet used.

Correct result:

I need your help

---

2. Brand Names Should Not Be Translated

Auto-translation loves to translate everything — including things it absolutely shouldn’t.

Examples:

• ChatGPT

• Discord

• React

• Next.js

If these get translated, the message becomes confusing fast.

I needed a way to protect proper nouns and technical terms.

---

3. Broken Characters & Formatting Issues

Another subtle issue:

Some platforms and fonts don’t handle all scripts perfectly.

That can lead to:

• Broken characters

• Random symbols

• Numbers appearing where text should be

To avoid this, I normalized output and ensured Unicode-safe formatting across the pipeline.

---

💡 The Heart of FlowTalk: The Translation Engine

At the center of everything is a small but powerful service: TranslationEngine.

Instead of scattering translation logic everywhere, I wrapped it in a single layer that handles:

• Language detection

• Caching

• Glossary protection

• Error handling

Here’s a simplified version:

// src/services/TranslationEngine.ts

import { LingoDotDevEngine } from "lingo.dev/sdk";

export class TranslationEngine {
  private lingoEngine: LingoDotDevEngine;

  constructor(apiKey: string) {
    this.lingoEngine = new LingoDotDevEngine({
      apiKey,
    });
  }

  async translateText(
    content: string,
    sourceLang: string,
    targetLang: string
  ): Promise<string> {
    if (sourceLang === targetLang) {
      return content;
    }

    const result = await this.lingoEngine.localizeText(content, {
      sourceLocale: sourceLang,
      targetLocale: targetLang,
    });

    return result;
  }
}

This is where Lingo.dev really shines.

Instead of simple word replacement, it provides context-aware translation, which makes conversations feel natural rather than robotic.

Chekout My Repo here: https://github.com/TejasRawool186/FlowTalk

special thanks to @sumitsaurabh927 @maxprilutskiy for the continious guidance