Content in Translation

Style Guide Automation: AP, Chicago, Duden Without the Manual Work

The AP Stylebook runs over 600 pages. The Chicago Manual of Style exceeds 1,100. Every translation into English should theoretically conform to one of these standards—but no translator holds hundreds of rules in active memory while working.

Style guide adherence in translation has traditionally been aspirational. Teams declare they follow AP or Chicago, reviewers catch obvious violations, and countless small deviations slip through because no one can enforce that much detail manually.

LQA Automation: The End of Manual Error Hunting

Quality assurance in translation has a fundamental problem: it requires humans to read everything.

Linguistic Quality Assessment (LQA) evaluates translation quality through systematic error detection and categorization. A reviewer reads each segment, identifies problems, classifies them by type and severity, and scores the overall quality. This produces valuable data about translation performance.

It’s also exhausting, time-consuming, and doesn’t scale.

The review fatigue problem

LQA reviewers face cognitive challenges that undermine quality:

Translation QA Metrics That Actually Matter

“The translation scored 92%.” What does that actually mean?

Quality metrics in translation suffer from a fundamental problem: they’re often abstract numbers disconnected from what matters. A project can score well on mechanical metrics while producing translations that don’t work for their intended purpose. Or score poorly on pedantic criteria while delivering effective communication.

Better metrics connect quality assessment to actual outcomes.

The problem with single-number scores

A single quality score collapses complex information into one figure:

Auto-Selection: Picking the Best Translation Automatically

Run the same content through three translation engines and you get three different translations. Sometimes they’re nearly identical. Sometimes they’re meaningfully different. Occasionally one is clearly better than the others.

How do you choose which one to use?

The multi-output reality

Modern translation workflows often produce multiple outputs:

• MT engine A (DeepL)
• MT engine B (Google)
• LLM translation (frontier or local models)
• AI-enhanced MT

For some segments, all four produce essentially the same result. For others, the variations matter. A human reviewer comparing all four versions for every segment would spend more time comparing than the translation itself takes.

From YouTube URL to Translated Video in One Workflow

You have a YouTube URL. You need that video in Spanish, German, and French. The manual workflow: download, transcribe, export to translation, translate, create subtitles, optionally dub, create three new videos.

That’s a lot of steps. Each one takes time, requires tool switching, and introduces potential errors in handoffs.

Modern video localization integrates these steps into a single workflow.

The fragmented video workflow

Traditional video localization involves:

Step 1: Acquisition. Download the video from YouTube. Need a third-party tool. Hope the quality is acceptable.

Batch Audio Transcription at Scale

Your organization has 500 audio recordings that need transcription. Maybe they’re customer calls for analysis, training recordings for localization, meeting recordings for documentation, or podcast episodes for subtitling.

Transcribing them one at a time would take weeks. And the real work starts after transcription: translation, subtitling, analysis, or whatever downstream process needs text from audio.

Batch processing makes audio transcription practical at scale.

The scale problem

Modern ASR (automatic speech recognition) processes audio in real-time or faster. A 10-minute recording transcribes in under 10 minutes. One recording is trivial.