How do you keep docs and UI strings translated as the source changes?
A localization AI Employee connected to your repository and your translation memory. When source docs or UI strings change, it translates just what moved, reuses your approved wording, and opens a PR in each target language for a reviewer to merge.
×Translations are always a step behind the source. An engineer updates a UI string or edits a docs page, the change ships in the base language, and the translated versions quietly fall out of sync. The gap is invisible until a customer reads a screen where half the labels are in the wrong language or a help page describes a flow that no longer exists. Catching up is tedious and easy to defer. Someone has to diff the source against each language, find exactly what changed, translate only those pieces, and keep the terminology consistent with everything already shipped: the product names, the tone, the phrasing decisions made months ago. That last part is the work translation memory exists for, and the part that gets lost when changes are retranslated by hand under deadline.
The localization PR AI Employee is triggered on a source change. It finds the docs and UI strings that moved since the last translated version, translates just those, and draws on your translation memory so approved wording and terminology stay consistent. It opens a pull request per target language for a reviewer to merge. Its only write is the translation branch, and it never merges or publishes.
See exactly how the work gets done.
Triggers on a source change
A change to the source docs or string files starts an isolated run inside your own environment with the repository permissions you set for it.
Carries your localization playbook
The target languages, which files are translatable, terms to leave untranslated, and tone travel with it as skills and memory.
Finds only what moved
It diffs the source against each language's last translated version and scopes the work to exactly the strings and passages that changed, not a full retranslation.
Reuses your translation memory
It looks up approved wording and terminology in your translation memory so product names, phrasing, and tone match everything you've already shipped. Credentials stay in memory, never exposed to the model.
Read on the repo, write only the branch
It reads the source and the existing translations; its only write is the translation branch it opens. It changes nothing else in the repository.
Opens a PR per language for review
The updated translations come back as a pull request in each target language, showing the source change alongside it. A reviewer owns the merge.
Runs in your environment
Every run is isolated inside your own infrastructure, reaching only the repository and translation memory it's scoped to. Your data never leaves.
Read on the repo, write only the branch
It reads the source and existing translations, and its only write is the translation branch it opens for review.
Never merges or publishes
It opens a PR per language; it never merges, releases, or ships a translation itself. The reviewer's signature is required to merge.
Credentials stay in memory
Repository and translation memory credentials live only in the run's memory, never written to disk and never exposed to the model.
You own every rule
The target languages, the do not translate terms, and the approved terminology are yours, versioned and changed on your terms, not in a vendor dashboard.
The translations that used to lag the source now arrive as a reviewable PR per language, scoped to exactly what changed and consistent with everything already shipped. Reviewers approve a focused diff instead of hunting for what fell out of sync.
On every source change
The strings and docs that moved are translated automatically
PR only
A reviewer owns the merge in each language
Consistent
Approved wording reused from your translation memory
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do AI Employees keep your docs in sync with the code?
Sweeps the code merged each day, rewrites the docs those changes touched, and opens one reviewable pull request. Publishing waits for a human merge.
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How do AI Employees test every pull request before a human reviews it?
Checks out every pull request, runs the suite, exercises the change through the edge on a test deploy, and posts the result. It stays off production, leaving the merge to a person.
×How do you get a postmortem drafted the moment an incident resolves?
Reconstructs the incident timeline, correlates it against deploys and log spikes, and opens a structured postmortem as a doc PR. It drafts, and the team finalizes.
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