Localization is one of those things every serious product eventually needs, but many teams still treat it as a separate, heavy process.
You extract strings from your app, send them somewhere, wait for translations, copy files back, fix formatting issues, review inconsistencies, and repeat the same process every time the product changes.
For developers, this often feels disconnected from the normal software delivery workflow.
That is exactly where doloc takes an interesting approach.
Instead of trying to become another large localization management platform with dashboards, complex workflows, and translator management, doloc focuses on a simpler idea:
What if localization could work like a DevOps task?
What is doloc?
doloc is an API-first localization tool for developers and engineering teams.
It takes your existing localization files, translates them using context-aware AI, and returns the result in the same structure and format, ready to review, commit, and ship.
The main idea is simple:
curl https://api.doloc.io \
-H "Authorization: Bearer $API_TOKEN" \
-F source="@source.en.json" \
-F target="@target.fr.json" \
-o target.fr.json
No complicated platform migration.
No heavy onboarding.
No new translation system to force into your stack.
You keep your files, your Git workflow, your CI/CD pipeline, and your existing development habits.
Why this matters for developers
Most modern engineering teams already have a workflow for everything:
- code is versioned in Git
- tests run in CI
- builds are automated
- deployments are scripted
- reviews happen through pull requests
But localization often remains outside that loop.
doloc brings translations closer to the engineering workflow. You can run it locally, add it after your string extraction step, or integrate it into CI so translations stay updated as your application evolves.
This makes i18n feel less like a separate project management process and more like a natural part of software delivery.
Key advantages of doloc
1. API-first by design
doloc does not force developers to use a dashboard for every translation task.
You can call the API directly using curl, scripts, or CI jobs. That makes it easy to automate and easy to integrate into existing pipelines.
For teams that prefer infrastructure-as-code, Git workflows, and reproducible processes, this is a big advantage.
2. Works with real localization file formats
doloc supports common localization formats such as:
- XLIFF 1.2
- XLIFF 2.0
- JSON
- ARB
- Android XML resources
- Java Properties files
This makes it useful across different stacks, including Angular, React Intl / FormatJS, Flutter, Android, Java, and other JSON or XLIFF-based setups.
Instead of exporting content into a separate tool and importing it back later, you can work directly with the files your app already uses.
3. Fits naturally into CI/CD
Because doloc is API-based, it can be added to your development lifecycle in different ways.
You can run it:
- locally before opening a pull request
- during CI checks
- after extracting new strings
- as part of a scheduled localization job
- before preparing a release
This is especially helpful for fast-moving products where UI copy changes frequently.
4. Keeps translation files in your repository
One of the strongest points of doloc is that your localization files stay in your repo.
That means translations can be reviewed like code.
You can check the git diff, open a pull request, review changes, and roll back if needed. For engineering teams, this is much cleaner than managing translations in a disconnected platform where the source of truth becomes unclear.
5. Context-aware translations
Basic machine translation can work for simple strings, but software localization needs consistency.
Buttons, error messages, empty states, onboarding screens, and product-specific wording need to follow the same tone.
doloc uses context from your existing translations to keep terminology and style more consistent across your app. This is especially useful when your project grows and contains many strings across multiple files.
6. Less platform overhead
Traditional localization platforms are powerful, but they are not always necessary.
Sometimes you do not need translator management, complex approval workflows, or a full dashboard. Sometimes you simply need to translate app strings quickly, review the output, and ship.
doloc is a good fit for that use case.
It gives developers a lightweight workflow without introducing a heavy process around localization.
7. Scales well with multiple languages
Another interesting point is the pricing model.
doloc pricing is based on unique source texts, not on the number of target languages or translation requests. So if your app has the same set of source keys and you want to translate them into several languages, you are not punished simply for expanding internationally.
For startups, open-source projects, and small teams, this can make localization easier to plan.
When should you consider doloc?
doloc is especially useful if:
- you are building a multilingual app
- your localization files are already in Git
- you want to automate translation updates
- you prefer API-based workflows
- you do not want to manage a heavy localization platform
- you want translations to fit into your PR and CI process
- your team cares about developer experience
It may not replace a full translation management platform if you need a large translator team, approval workflows, or advanced project management features.
But for many developer-led teams, that is exactly the point.
doloc focuses on making localization simple, automated, and close to the code.
Final thoughts
Localization should not slow down product development.
For many teams, the ideal workflow is not another dashboard. It is a simple, reliable process that fits into the tools developers already use every day.
That is what makes doloc interesting.
It treats i18n as part of the development pipeline, not as a separate operational burden.
If you are building a multilingual product and want a developer-friendly way to keep translations up to date, doloc is definitely worth trying.
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