Context Sensitive Translate meshes Translation Memories, Machine Translations, and Glossaries together into a single smart, contextualized feed. It learns with you and from you, improving quality and speed at the same time.

If you want the simplest summary, it acts like a smart blender that combines everything you already know into suggestions that get better as the project progresses.

We analyzed 4.3 million translated segments to measure how Context Sensitivity compares to standard machine translation workflows. The result was a productivity increase of 39% in the segments we studied.

You can explore the research and judge the methodology for yourself, but the outcome is simple: translators move faster when the system reflects their real preferences instead of forcing generic output.

Translators should not have to make the same correction again and again. With Context Sensitivity, every segment interaction informs the next suggestion in terminology, tone, and phrasing. The translator shapes the engine’s output as they go, which creates a stronger sense of authorship and reduces repetitive editing.

This is one of the reasons translators respond more positively to Context Sensitivity than to typical post-editing flows.

Syntax is about how a sentence is structured and how elements relate to each other. Semantics is about what the sentence means in context and what it implies. Translation quality depends on meaning first, not just grammatical correctness.

Context Sensitivity performs semantic analysis because it evaluates words within context, allowing translators to choose the best match based on style, thematic references, and genre.