OneSky Localization Agent (OLA) is an AI localization and translation platform for software, apps, games, and other content, focused on centralizing translation workflows that are scattered across different teams and different AI tools, so that every document, website, app, and marketing campaign maintains a consistent brand voice.
In terms of use, OLA is used for automated AI translation and localization, with a focus on web and mobile apps. It can handle websites, marketing materials, documentation, and database content, and includes built-in translation quality assessment (QA). The platform supports more than 100 languages and more than 800 dialects.
Main capabilities:
- Best-of-many-models selection: It does not rely on a single off-the-shelf large language model. Instead, it uses multiple context-aware models specialized for project context and knowledge bases, then uses a built-in LQA algorithm to select the best translation. The company says its accuracy is 20% higher than standard AI translation tools.
- Role-based agent team: AI agents mimic the role division of a real translation agency, including an AI localization manager (adds context based on style guides, preferences, and cross-string attributes), AI translators, AI voters (vote on multiple candidate translations of the same source text to select the most suitable one), AI reviewers (review context, verify translation formatting, and make edits), and AI evaluators (screen reviewed translations using machine translation quality scoring algorithm metrics and flag items needing follow-up), and feed back in a loop until the AI can no longer further optimize.
- Brand consistency: By enforcing glossaries, style guides, and brand rules, it reduces the fragmentation in tone and quality caused by different teams each using their own AI tools.
- Automated execution and workflow integration: Triggered by connected system services, it runs automatically after source text is imported; it supports automatically generating glossary entries, accepting feedback for optimization, outputting translation quality reports and localization insights, correcting translation issues, and flagging unfixable items for human review and post-editing.
- Dashboards and reports: Manage and track AI team performance through dashboards, and receive email summaries that aggregate progress, quality, and risks.
- Format support: Handles more than 30 string file formats, covering iOS (.plist, .strings, .stringsdict, .xcstrings), Android (.xml), Web (.hjson, .ini, .js, .json, .mjson, .php, .po, .properties, .resx, .ts, .xliff/.xlf, .xml, .yml/.yaml), as well as document formats (.tmx, .txt, .xls, .xlsx, etc.).
- Translation memory: Supports mainstream translation memory formats, can migrate existing translation databases and reuse them to improve the AI translation quality of dedicated projects.
- In-platform editing: Source text and translations can be managed directly in the platform, with built-in features such as glossaries, translation memory, and contextual screenshots to improve quality.
How to use it: Strings files can also be submitted manually without integration. Simply provide context and configure workflow automation in the platform, and AI agents will run autonomously after the source text is imported; after the task is completed, translations can be exported, or flagged translations can be followed up on through the platform. If you want translations to be more natural and closer to human fluency, you can connect a post-editing service to polish the translations flagged by the system.
Use cases: Suitable for teams with high content costs or difficulty scaling localization, replacing part of the manual process with an AI team, maintaining consistent accuracy from pre-translation and translation to post-translation stages, and entering global markets faster at lower cost.
Limitations: The materials do not provide specific billing and pricing information, so it is not available for now; compliance details such as whether offline deployment and data residency are supported are not available for now; obtaining the best results requires providing sufficient project context, style guides, and preferences; flagged translations may still require human review or post-editing.
