The AI Ready Localizer #6: One Month with Lovable

Building What I Couldn’t Walk Away From

Lovable was born out of the belief that anyone with a clear idea and a bit of technical curiosity should be able to build powerful tools without drowning in complexity. Founded by a small team of engineers and designers in Sweden, Lovable quietly built one of the most elegant AI full-stack platforms in Europe. What started as an internal experiment quickly gained traction among indie makers and fast-moving teams who wanted to turn ideas into software, fast. By combining modular building blocks with deep AI integration, Lovable is redefining how products are prototyped, tested, and launched.

Anton Osika and Fabian Hedin are tinkerers at heart. Anton grew up in Sweden, taught himself to code young, and gravitated toward the intersection of math, physics, and machine learning, eventually co-founding AI startups before Lovable. Fabian’s path was equally hands-on, contributing to accessibility tech like Stephen Hawking’s interface and working with SpaceX engineers on mobility projects. Both share a deep belief that technology should be fast, useful, and human, driven less by flash and more by function. Lovable is their way of making that vision real.

In just a few months, Lovable’s gone from stealth mode to the AI platform every builder, tinkerer, and domain expert is buzzing about. A small, brilliant team out of Sweden has crafted a full-stack AI dev environment that feels like magic: zero bloat, zero barriers. Just clean interfaces, robust orchestration, and lightning-fast speed.

Here’s the wildest part: if you know how to prompt well and can debug a little, you can go from idea to working app in minutes. Not hours. Minutes.

What’s most important is knowing how to improve a system, not just how to execute development. Lovable empowers you to experiment, iterate, and adapt on the fly. You just need to be curious and willing to explore.

This was the place where I could finally build the things I’ve been obsessing over for years. Not just MVPs. Not just side projects. Tools I couldn’t walk away from—because they solve problems I care deeply about, challenges I’ve lived with professionally for over a decade.

So I started.

I built three projects. Each one designed to tackle the friction points I see every day in internationalisation, localisation, and content operations at scale. And for the first time, I didn’t need to wait for budget, approval, or engineers. I just… built.

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🚀 Tool #1: The Localization Hub

This tool stays fully in sync with your GitHub repo—it auto-imports new source strings from each sprint and pushes localized files back when translations are ready. It also attaches Figma visuals at the segment level, and lets you batch-draft translations using AI.

There's a second step too: AI-powered post-editing based on your custom instructions, with support for glossaries and style guides per locale for extra consistency and control.

Want more? Upload your final localized designs and run visual QA directly on the platform—no extra tools needed.

Bonus: built-in sprint analytics show how your localization is evolving. Which locales need attention? Who’s stuck? What’s gaining traction? It’s all visible, in real time.

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🛠️ Tool #2: i18n Issues Scanner

Drop in your JSON files. Get a structured report. Done.

It flags hardcoded strings, concatenations, missing plural logic, date/number format issues—all the little gremlins that slip through and haunt downstream localization.

Now, engineering and localisation teams can have a real conversation before strings go live.

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🌐 Tool #3: Multilingual Glossary Manager (for Everyone, Not Just Loc)

This one’s close to my heart.

I’ve always dreamed of a terminology platform that:

• Serves product, marketing, legal, and support—not just localisation teams

• Flags translation mismatches and checks product/feature availability by market

• Offers collaborative validation workflows (linguists propose, market owners approve—with visual context!)

• Feeds approved terms into LLMs via API for on-brand content creation

No more messy spreadsheets, email chains, or version control nightmares. Just one scalable source of truth.

It still needs some work to be fully functional, but the main structure is in place — and I’m really happy with how it’s shaping up.

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You know what’s best? When the three tools don’t just exist side by side but actually work together in a smart, orchestrated workflow—whether through pre-defined triggers or more advanced agentic setups.

Imagine this: the i18n scanner checks the codebase in GitHub. Only when it confirms that all strings are properly externalised and no internationalisation issues remain, it greenlights the next step. That’s when the L10N Hub kicks in, launching the localisation process with clean, compliant files.

But it doesn’t stop there. The post-editing phase inside L10N Hub pulls live data from the Glossary API, ensuring terminology consistency from the start. No more retroactive glossary checks or manual fixes. And behind the scenes, each tool communicates through lightweight APIs or agents that know when to escalate, when to continue, and when to involve humans.

It’s lean, it’s smart, and it’s how localisation should be done.

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And what’s next?

I’ve been saying it for a while now: we need to stop translating marketing. Honestly, we should’ve never started.

I want to tackle marketing campaign creation: a multilingual content engine that doesn’t translate, but crafts native-market messaging based on real research and creative insight.

It presents you with several options tailored to your instructions—you can choose the one you prefer and even edit it to your liking.

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TMS platforms have rarely met the specific needs of organisations—instead, teams have spent years bending their workflows to fit the tool, rather than shaping tools around their workflows.

Now, with vibe coding and a new wave of technical talent, professionals can finally build customised, purpose-fit localisation tools that adapt to their exact needs—not the other way around.

Lovable gave me a platform. But more than that—it gave me momentum.

Because when you care deeply, and the tooling gets out of your way, the only limit is how fast your fingers can type.

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But Julia, really—how much technical knowledge did you need to build all those tools in less than a month?