TextUnited

If your team reviews translations but nothing improves over time… you’re not improving quality, you’re just fixing files. You’re missing the layer that makes AI translation better with every project: Automatic Post-Editing (APE). APE doesn’t just clean up machine translation (MT) It systematically improves output quality: enhancing fluency, consistency, and style by learning from every correction your team makes. So instead of repeating the same fixes, your translation quality improves over time with a smooth workflow inside the Translation Management System (TMS) such as TextUnited. Curious how that works in practice? This article breaks this down, and including how to use it in TextUnited. https://lnkd.in/gQX9kBXi

We keep hearing this across manufacturing teams that “more content pressure” is real. More product pages, more materials for sales, more versions for different markets. And now, with AI, it finally feels doable. Content gets created faster, translated faster, adapted faster. It’s not surprising. Most industry reports point in the same direction – content demand is growing, and AI is already part of how teams try to keep up. So naturally, the expectation is simple: more content should lead to better results. But something doesn’t quite add up. And this is where another pattern shows up in those same reports – despite all the investment in content and AI, many teams still describe their results as only moderately effective. ✅ So maybe the real challenge is not about creating more content. Most teams are already solving that. 🎯 The harder part is staying confident in what that growing body of content actually communicates, once it starts to spread across languages, formats, and teams. 

Who actually owns translation when no one owns translation? There’s a moment in many companies where the need for translation just... appears. And suddenly someone from marketing or content is asked to “handle it.” Just because it needs to be done. So, they do what works. They send files to an agency. They try AI. Sometimes it’s fast. Sometimes it’s not.  Sometimes it’s good. Sometimes it needs fixing.  ⚠️ And most of the time, it’s handled ad hoc. Which is fine… until it isn’t. Because the more content you have, the more mess is around it. And that’s where things quietly get expensive. Not just in money, but in time and back-and-forth. 💡 That’s usually the moment when translation needs to stop being something you handle…  and start being something you set up properly. 

‼️ Myth: If someone reviews the translation, you’re safe.  You translate a training deck with AI and send the PowerPoint to a colleague in Italy. They open it, switch on track changes, make corrections, send it back.  Done. But those corrections live inside one version of one file. Next time you translate a similar deck, none of it is there. So you either:  - fix the same things again or  - try to remember what was already corrected or  - manually update your translation memory somewhere on the side And that’s the part no one really has time for. That’s the gap between reviewing content and actually improving it over time. Now imagine a slightly different situation. 💡 Instead of sending files around, you invite that colleague directly into the workspace where the translation happens. Their corrections don’t disappear into PowerPoint. They stay with the content and show up the next time you use it. Same effort - very different outcomes. If getting internal reviews feels like chasing people and losing their input along the way, we should talk. Let's run short, practical sessions where we show how to involve your colleagues without adding extra friction for you and them.

The biggest shift in technical training is the end of “the course” as we know it. Most teams still build training the same way: you create a course, export it to SCORM, upload to LMS, translate it into a few languages… and move on. Let’s talk about the future of training today. AI changes the model we know. People don’t want to go through courses anymore. They expect answers in real time, inside the workflow, in their own language. In a knowledge base, app or chat. So who knows? Maybe the role or course materials will change soon? Maybe they no longer just will be something people go through. Maybe they will become the source behind every answer. But! AI doesn’t create knowledge. It pulls from what already exists. So if training content could be kept consistent across languages, updated once instead of in every version and reused across courses, manuals, systems and AI solutions... Then something big is coming, don’t you think? — Curious how technical training content turns into usable language data?  Let’s talk. 

"We get content translated centrally… and then I end up correcting it anyway." Sounds familiar? Most local "fixes" disappear into email chains and comments in the files, only to be repeated in the next project. At TextUnited, we see this as the #1 silent killer of global content scaling. We’re diving deep into this "post-translation chaos" in our latest newsletter. If you're tired of manual fixes, this one is for you. 👇 Check it out.

Expanding into new markets usually starts with a simple step: translate what you already have. The website, decks, proposals, product sheets... Each piece gets done. And at first, it works. But over time, small differences start to appear. A phrase changed here. A claim adjusted there. Something rewritten to “sound more natural.” Each decision makes sense on its own. At small scale - 3 or 4 markets - this is manageable. At a larger scale, it starts to drift away. AI lets you create and adapt content faster than ever. But in international sales, wording is not just wording. It’s what you promise in each market. ⚠️ If language is just an ad-hoc task, you get output. And it differs each time.

Most technical teams don’t have an AI problem; they have a process problem. We often hear: “We’re building an internal tool.”  “We’re testing our own solution.”  “Process changes are hard to implement.”  Understandable. Because when documentation supports regulatory compliance, export, product declarations, and safety instructions, AI cannot be an add-on. It has to operate inside a controlled system. That’s why we’ve just published:  How teams use AI safely on complex and regulated content Read here: https://shorturl.at/iBcID We didn’t write about speed, but about terminology enforcement, workflow traceability and embedding AI into existing processes. In regulated environments, fluency is not the benchmark. Control is.

We recently spoke with someone responsible for technical documentation in a manufacturing company. The biggest issues? Declarations of Performance, ETA documents, terminology. Sometimes a “screw” becomes a “bolt”. Looks minor. But you know it isn’t. In technical documentation, that’s a different component. Here’s what their process looked like:  – Mostly outsourced with no dedicated system  – Even with technical translators, the internal company team still had bigger product knowledge  – So they did manual corrections at the end either way  And the same conclusion every time: “We still have to fix everything ourselves.” ⚠️ This wasn't a translator or agency problem. It was a terminology control problem. If AI and humans don't operate inside an approved terminology, the result is a document that is fluent but systemically wrong. And you need confidence in every word.

A product update has been released. Specs change. UI text changes. Documentation changes. And AI retranslates everything. Now imagine three people from three different teams interpret a couple of terms slightly differently. Or use a synonym. Correct, but different. ⚠️ Who catches it? And how to prevent it? Distributing tasks feels efficient. Until something breaks and gets published. 💡 In large organizations, language is not just content. It’s data.  And data needs supervision and validation.