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Creating and Running 22 AI Language Learning Apps. Crazy Mistake or Genius Plan?

Hi everyone 👋
My name is Alex. I'm an indiehacker by night, struggling to find more time for my own projects. If that sounds familiar, you're probably one of us.

The backstory

For the last 6 months, I've been deep into a pretty complex AI B2B project. I love it — still working on it when I can — but I hit a wall. There's simply no way to give it the time it deserves while holding down a full-time job.

The obvious path? Find investors. But let's be real — that road is long. Business plans, pitch decks, endless emails, calls, rejections, more calls... It requires almost as much time as building the product itself.
So I made a decision: build something I can monetize quickly.

Why language learning?

I'm currently learning my third language, and I've become obsessed with how we actually acquire languages. Not the textbook way — the natural way.
Think about how children learn. They listen. They absorb. They read. They pick up words from context and gradually understand more and more. No flashcard grinding. No conjugation tables. Just pure comprehension.

Understanding is everything. If you don't understand, you won't speak. You won't write. Comprehension comes first — it's the foundation everything else is built on.

That's the philosophy behind Erla.

The market reality

Language learning is a massive market. But it's also brutally competitive. Duolingo, Babbel, Busuu — the giants are everywhere.
So I asked myself: How do I even get discovered?
My answer: Don't build one app. Build 22.
One app for each language. Why? App Store Optimization. When someone searches "Learn Polish" or "Learn Finnish," a dedicated app ranks better than a generic "learn any language" app. Each app becomes its own marketing channel.

The automation challenge
Now you're probably thinking: "22 apps? That's insane. How do you even manage that?"

Here's the breakdown:

  • 22 apps on Google Play
  • 22 apps on App Store
  • ~30 localizations per app
  • = 1,320 store listings total

Sounds like a nightmare, right?

But I built automation for everything:

  • 🖼️ Screenshot generation
  • 🌍 Translation pipelines
  • 📤 Automated uploads to both stores
  • 🔄 Bulk metadata updates

Result: A full release across all 1,320 listings takes about 4 hours.

Where I'm at now

I uploaded the apps just a few days ago. Already seeing organic installs trickling in — no paid marketing yet.

The app is simple right now. Two modules:

  • 📖 Reading — Interactive short stories with tap-to-reveal grammar and translations
  • 🎧 Listening — Native audio with a "guess first, reveal later" methodology

It's free for now while I build momentum.

The roadmap

I'm building Erla into a super app for natural language learning:

  • AI conversation partner (chat with an AI teacher)
  • Grammar modules
  • Vocabulary building through context
  • Progress tracking
  • And more...

All powered by AI. All focused on comprehension-first learning.

The goal

I'll be honest with you — my goal is simple: freedom.
I want to hit $10K MRR across all 22 apps within the next year. That's roughly $450/month per language. Ambitious? Maybe. But with 22 shots at the target instead of one, the math starts to work in my favor.

What do you think?

Am I crazy for managing 22 apps? Is the "one app per language" strategy smart ASO or operational suicide? I'd love to hear your thoughts.

If you want to check it out:
🔍 Search "Erla" in the App Store or Google Play
🌐 Or visit: https://erla.app

Happy to answer any questions about the tech stack, automation setup, or the journey so far.

Let's build! 🚀

posted to Icon for group Building in Public
Building in Public
on December 20, 2025
  1. 2

    That’s an interesting bet — running many small apps feels like a way to let the market decide for you.

    Curious — across the 22, what’s the one signal you rely on to decide which apps deserve more attention (retention, revenue, usage frequency), and which ones you let fade?

    1. 1

      For now I am trying to give the same amount of love for every app, later probably will have to preoritize some of them.

      1. 2

        That’s fair — giving everything equal attention early makes sense.

        One pattern I’ve seen help in portfolios like this is letting behavior choose for you instead of effort: which apps start pulling you back in without forcing yourself to “care” about them.

        Often the first signal isn’t revenue, but which ones create friction when you don’t work on them for a bit.

  2. 2

    This is a really interesting GTM wedge — especially the automation layer, which changes the usual “too many apps” argument.

    The part that feels most important to me isn’t whether 22 apps is crazy, but how you’ll know early if this strategy is worth continuing. ASO can work, but it can also mask focus risk if signal is noisy.

    Curious what you’re watching most closely over the next 60–90 days — installs per language, retention by cohort, or which languages show disproportionate pull? That seems like the decision point for whether this scales or needs tightening.

    1. 1

      I am bringing paid features right now so the most important part would be MRR per app.

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