Case study:
k+
words
languages
weeks
people
This job had around 94,000 words of content per language and was to be delivered in 4 weeks.
We saw that this would require some resource testing to find the best translator match for this project. We formed 3 teams of translators and submitted their work samples to the client for review. Thus, the best-matching team was identified and assigned as the primary team, keeping the 2 other teams as backup to ensure a continuous workflow. Next, we identified the best platform for user interface (*.json) and technical documentation (*.md) files. Translation was done in 2 platforms, while constantly updating the TMs to keep consistency between the user interface and documentation.
When starting to localize the technical documentation, a new challenge arose: we noticed a lot of corrupt and improperly displayed content due to suboptimal CAT configuration, with no predefined way to process markdown files. Our project manager and localization engineer developed a ruleset to apply to the CAT so it would display the files properly.
With a bit of trial and error, both parts were finalized for both languages. Currently we are maintaining both of them, and the client is planning to add more languages in 2023.
This job required us to process around 32,000 words and to produce 157 minutes of subtitles in 16 languages.
We took part in a milestone AI training project, where the goal was to make an AI capable of translating conversational language from Russian into English by means of analyzing and learning from human pre-translated tweets and articles.
The project goal was to introduce a localization infrastructure for the client’s product platform to effectively scale it from 2 to 16 languages. Each new language added 220,000 words of translated content, with a desired turnaround time of 4 weeks.
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