Transifex now sells one thing: AI translation of your strings, grounded in a translation memory, glossary and style guide, with a quality score deciding what can skip review. For a single developer that is a script and a small review page: read the locale files, pull memory and glossary matches, ask an LLM, score the result, approve what clears a threshold and queue the rest. An agent builds that in one sitting. The other half of the platform is people and plumbing: agencies and community translators working in a browser, branching workflows, Figma and Zendesk connectors, and over-the-air delivery to apps.
Build me a local AI localization pipeline to replace Transifex for my app's strings. Requirements: - Node.js 22 + TypeScript: a CLI called `l10n` and a Fastify review page on http://localhost:4710. - Formats: read and write i18next JSON, gettext .po (`gettext-parser`), Android strings.xml (`fast-xml-parser`) and Apple .strings in place, keeping key order and comments. LOCALES_DIR, SOURCE_LANG and TARGET_LANGS are set in .env. - Storage: better-sqlite3 in ./data/l10n.db for strings, translations with a status (draft, approved, needs-review), a translation memory of approved pairs and a glossary per language. - Translate: `l10n translate` sends only missing or changed strings, 20 per batch, to an OpenAI-compatible endpoint (LLM_BASE_URL, LLM_API_KEY, LLM_MODEL in .env). Each request includes exact memory hits, fuzzy hits at 80% or more (`fastest-levenshtein`), the glossary terms that occur in the string and the text of ./style-guide.md. - Quality score 0-100 per string: deterministic checks (placeholders, HTML tags, ICU plurals through `@messageformat/parser`, length ratio) plus one judge prompt rating accuracy and tone. A string at or above QUALITY_THRESHOLD (default 85) that passes every check is approved automatically; the rest become needs-review. - Review page: the needs-review queue with source, draft, score, reasons and memory matches; edit and approve, where approvals feed the memory, and a "never translate" flag for glossary terms. - XLIFF 1.2 and TMX export and import, so an agency can work in its own tool. - Single user. No accounts, no telemetry, local-first: only the model calls leave the machine. - Out of scope: translator logins, Git host integrations, Figma and CMS connectors, over-the-air delivery. - README: setup, the .env keys, where the data lives, and a note that the judge score is a heuristic to spot-check by hand for each new language.
Opens your agent with the prompt filled in, you press enter. Or copy it and paste it into an empty folder.
The first prompt builds the core. Run these one at a time in the same folder to grow it into your own Transifex.
Add a Transifex importer to the localization pipeline in this folder. Requirements: - `l10n import-tmx <file.tmx>` loads every translation unit into the translation memory with its language pair and skips duplicates, so running it twice is safe. - `l10n import-glossary <file.csv>` reads the Transifex glossary CSV (term plus one column per language) into the glossary, keeping notes where present. - Print a summary of new, duplicate and skipped entries before writing, and ask to confirm. - Mark imported memory entries as approved. Update the README with the two Transifex export steps.
Add a GitHub Actions workflow to the localization pipeline in this folder. Requirements: - On pushes that change the source language file, run `l10n translate` and commit the target files to a branch named l10n/<date>; open a pull request with the GitHub CLI. - Put the number of auto-approved and needs-review strings in the PR body, and fail the check if any placeholder check fails. - Secrets come from repository secrets, never the repo; never commit to the default branch. - Update the README.
Add a calibration command to the localization pipeline in this folder. Requirements: - `l10n calibrate <lang> --sample 30` picks 30 auto-approved strings at random and shows them in the review page with the score hidden; I mark each as good or bad. - Compute the share of good strings by score band (below 70, 70-84, 85-94, 95 and up) and print a suggested QUALITY_THRESHOLD; store every run in SQLite. - Update the README.
Teams want new strings to turn into shipped languages without a developer in the loop: Git sync brings source text in, AI drafts and scores it, reviewers fix what is flagged and the result goes out to apps. Paying also buys agency access and a record of who approved what.
Checked 2026-10-19 against Transifex's docs: 1, 2, 3, 4, 5, 6, 7
| Plan | Monthly | Annual, per month | What you get |
|---|---|---|---|
| Scale | custom | — | Unlimited collaborators, hosted content and projects; AI translation with quality scoring, 46+ file formats, Git integrations, SDKs, CLI and over-the-air delivery, SSO and 2FA. |
Yes. An AI coding agent such as Claude Code, Codex or Cursor can build a usable personal version in one session with the prompt on this page, running on your own machine.
Transifex's pricing is usage-based or varies by plan.
A browser workspace where agencies, freelancers and community translators work with roles; Transifex's own quality index, tuned across languages; your judge is just another prompt; Connectors for Figma, Sketch, Contentful, Zendesk, HubSpot and Slack; Transifex Live for translating a website without a deploy, and over-the-air updates to apps; A partner network for human review. If any of those matter to you, keep paying.
Yes: Weblate (open source), Tolgee (open source), Pontoon (open source).