Content Translation & Localization

Part of the worlds-biggest-software-project initiative.

An AI-native, open translation management platform that combines machine translation, human review workflows, and terminology governance for global software teams.

Content Translation & Localization is a translation management system (TMS) for product, engineering, and localization teams shipping software to multiple regions. It unifies machine translation, translator collaboration, glossary and terminology enforcement, and continuous-localization pipelines. The core problem it solves: existing TMS platforms are either expensive, closed, or split MT quality from workflow tooling — leaving teams to stitch together pipelines and pay enterprise prices for adaptive AI.

Why Content Translation & Localization?

Incumbents are expensive at scale. Lokalise's 2025 pricing introduced word-count caps and tiers up to $990/mo; Smartling and LILT are enterprise-only at $15K–$100K+/yr with no self-serve entry point.
Adaptive AI is gated behind enterprise contracts. LILT's adaptive learning loop is the standout AI capability in the market, but it requires professional services and enterprise onboarding.
Open-source options have gaps. Weblate (AGPL-3.0) is solid for Git-native workflows but lighter on enterprise workflow automation and lacks design-tool integration.
Translation-only engines miss the workflow. DeepL Pro delivers strong NMT quality but offers no TMS — no translation memory, no collaboration, no review workflows.
No incumbent closes the correction feedback loop automatically across glossaries, MT, and reviewer corrections in a single open platform.

Key Features

Translation Memory & Terminology

Translation memory with reuse across projects and language pairs
Glossary and termbase management aligned with TBX (ISO 30042)
Automatic term enforcement across human and AI translation modes
Cross-project terminology harmonization for multi-product organizations

AI Translation & Quality

Multi-engine routing across LLM and NMT providers (OpenAI, DeepL, Google, Anthropic, Mistral, and others)
Adaptive learning loop that improves suggestions from reviewer corrections in real time
ML-based quality estimation to score per-segment confidence and prioritize review queues
Risk-based triage that auto-approves low-risk segments and routes high-risk content to humans
Cultural adaptation suggestions that flag idioms, humor, and regional imagery beyond literal translation

Collaboration & Workflow

Real-time multi-user editing with live notifications, commenting, and change history
In-context visual editing with overlay mode for strings shown in situ within the UI
Codeless workflow automation (drag-and-drop) for non-engineers building approval hierarchies
Built-in client review and version control

Developer & Designer Integration

Continuous localization via Git/CI-CD with webhooks, GitHub Actions, and VCS sync
Design-tool integration starting with Figma for translate-before-code workflows
SDKs for web and mobile with optional over-the-air translation delivery
30+ file format export including XLIFF, JSON, Android, iOS, YAML, gettext

Quality Assurance

Rule-based QA for placeholders, markup, punctuation, and consistency
ML-based quality estimation trained on past reviewer corrections
Compliance audit trails with sign-off workflows for regulated industries

AI-Native Advantage

Unlike incumbents that bolt MT onto rule-based workflows, this project closes the correction feedback loop: every reviewer edit improves future suggestions for that brand voice, domain, and terminology automatically. AI-driven risk classifiers triage segments so low-risk content auto-approves while legal, marketing, and brand-critical strings reach the right human reviewer. Cultural adaptation goes beyond word-for-word translation, surfacing idioms and imagery that won't land in the target market. Figma-time translation shortens the i18n feedback loop by weeks compared to post-build localization.

Tech Stack & Deployment

The platform targets both self-hosted and managed deployment, drawing on Weblate's data-sovereignty model while matching the workflow automation depth of Phrase and Smartling. It honours industry standards — XLIFF (ISO 21720), TMX, TBX (ISO 30042), Unicode CLDR, ISO 17100 quality requirements, and ISO 18587 MTPE — to ensure portability and avoid lock-in. Integrations include Git providers (GitHub, GitLab, Bitbucket), design tools (Figma), CMS platforms, and mobile SDKs (iOS, Android, React Native, Flutter). A REST API and webhooks support custom automation; a CLI supports CI/CD pipelines.

Market Context

The global localization software market is projected at USD 2.33 billion in 2025, growing to USD 4.22 billion by 2033 (~7.5% CAGR), and the broader translation management systems market is expected to reach USD 5.47 billion by 2030 at 17.2% CAGR (Grand View Research, 2025). Incumbent pricing ranges from $13/mo (POEditor) and $59–$140/mo (Crowdin, Lokalise entry tiers) up to $15K–$100K+/yr enterprise contracts (Smartling, LILT). Primary buyers are localization managers at global software companies, heads of international product, marketing localization teams, mobile app developers, and game studios.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.