Translate without guesswork. Lokalise turns your visual frames, style guides, translation memory, and its proprietary AI stack into structured and precise context that helps linguists and Custom AI Profiles reach 90%+ acceptance rates.
3000+ teams going global with products that feel local
Context breaks when it’s scattered. Lokalise centralizes visual cues, language rules, and structured metadata that linguists and AI need to translate consistently.
Custom AI Profiles treat your glossary as an unbreakable rule, ensuring technical terms and product names are 100% accurate across 60+ languages.
Turn a style guide into structured rules and examples so Custom AI Profiles follow the same tone, inclusive language, and formatting in every language.
Custom AI Profiles use RAG to pull approved translation memory (TM) entries for each new string, keeping terminology and phrasing consistent across files and releases.
Lokalise recognizes text on uploaded screenshots and automatically links it to the right keys. Translators see the exact string location on the screen, which reduces guesswork and cuts review fixes.
Figma, Adobe XD, and Sketch plugins push design frames and screenshots into Lokalise. Translators and reviewers get the right visual context without chasing links or manual uploads.
Lokalise comments attach directly to a specific key or translation, so instructions stay tied to the string that needs them. Use comments for intent, UI constraints, and edge cases that do not show up in a screenshot.
Live in-context previews and Figma screenshots show linguists exactly where text appears in the UI, what surrounds it, and how much space they have to work with. That context helps them make better calls on tone, terminology, and phrasing before strings ever go live. Combined with the glossary and style guide, it reduces ambiguity upfront and leads to fewer LQA issues and less back-and-forth.
AI does not learn from screenshots. It needs structured inputs. Lokalise feeds Custom AI Profiles the glossary, approved translation memory, and any key descriptions or task instructions. Style guides get converted into clear rules and examples AI can follow. Under the hood, Lokalise’s MCP server orchestrates model selection, so teams can route work to the base models, private models, or specialized LLMs that best fit the task.
FAQs
What is the context deficit in localization?
Context deficit in localization happens when translators or AI work with isolated strings without seeing where text appears in the UI or what it is meant to do. That leads to wrong tone, truncation, and extra rework. Lokalise reduces this gap by connecting strings to design and product context, including Figma and in-context previews.
How does RAG improve translation quality?
Retrieval-Augmented Generation (RAG) improves translation quality by retrieving relevant context, such as glossary terms and approved translation memory, before AI generates output. In Lokalise, Custom AI Profiles use RAG so AI follows your approved terminology and phrasing
Is my style guide compatible with AI?
Most style guides need a rewrite for AI. Humans can interpret narrative guidance, while AI needs clear rules and examples. Lokalise AI-proofing turns style guidance into structured instructions so Custom AI Profiles can apply tone, inclusive language, and formatting consistently.
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