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AI-Assisted Translation. Engineered human control.

An AI draft is cheap. Knowing where that draft fails in your language pair is not.

We call our AI-assisted workflow SmartEdit. The AI produces the first draft inside memoQ or SDL Trados, with your translation memory and glossary already loaded. A specialist then works through every segment. What that specialist actually corrects depends heavily on the language, and this is where most of the value sits.

When translation is not a one-off job
Recurring releases do not fit a project-by-project handover. For clients on a continuous release cycle we work inside the environment their cycle already runs on, including the client's own, and keep the terminology and translation memory consistent across it.


▮Where the AI Draft Breaks in Turkish

AI draft quality is not the same across languages. English to German or English to French drafts arrive in reasonable shape. English to Turkish drafts arrive with recurring, predictable problems, and a reviewer who does not know what to look for will read past them.
 
The ones we correct most often:
  • Dropped subjects. Turkish allows the subject to be omitted. AI drafts frequently omit it where the source sentence has a specific actor, and the instruction becomes ambiguous about who performs the action. In safety and maintenance content this is not a style issue.
  • Long source sentences left intact. English technical writing chains relative clauses. Turkish is agglutinative and left-branching, so the same structure produces a sentence the reader has to reconstruct backwards. The draft is not wrong, it is unusable. It has to be split.
  • Terminology drift between TSE and EU sources. Many technical terms have one form in Turkish standards and another in EU-derived documentation. The AI picks whichever is more frequent online. Which one is correct depends on where your document is going, and that is a decision, not a lookup.
  • Register collapse in the second person. Turkish marks formality. Drafts move between formal and informal address inside the same document, especially in UI strings and user manuals.
  • English compound nouns. Three-word compounds get rendered word by word instead of resolved into the established Turkish term, if one exists.
We work in over 40 languages. We say this about Turkish specifically because it is where our own review data is deepest and where we can tell you in advance what the specialist will be doing.


We work directly with
Solplanet / AISWEI, Hoogendoorn Growth Management, Delta Plus Group, HI-REZ Studios, Yum! Brands, Subway, Toyota, Siemens Gamesa, Fluke, Faro, Xerox, TP-Link, OSCE, Umweltbundesamt, American Bar Association, Playata / European Games Group and many others across energy, horticulture, safety equipment, gaming, automotive, food, and public sectors since 2002.

Through our LSP partnerships, our work reaches pharmaceutical, technology, financial services, and media companies worldwide.
→ View our case studies

 

▮How SmartEdit Works
  • Asset preparation. Glossary, translation memory, and reference material loaded before anything is generated.
  • AI draft. Generated inside memoQ or SDL Trados through an API connection, not as a standalone tool, with your TM and glossary applied from the first segment.
  • Specialist review. Every segment read and corrected by a translator working in their subject area.
  • Automated QA. Tags, placeholders, numbers, units, and formatting checked.
  • Delivery. Files returned in your working format, with the TM and glossary updated for your nxt project.
▮We Assess Before We Recommend
Not every file is a good SmartEdit file. Before we quote, we run a feasibility check on your actual content at no charge: we look at the source quality, the language pair, the terminology situation, and what the document is for.
 
If the answer is that AI drafting will not save you anything useful here, we say so and quote the fully human workflow instead. That conversation costs us a faster job and saves you a bad one.
 
▮SmartEdit Pro
SmartEdit Pro adds an independent second reviewer after the first review is complete, followed by a harmonisation pass across the whole document.
 
It exists for one reason: a single reviewer working through a long file gets used to their own decisions. A second reader who has not seen the file before catches what familiarity hides, mainly inconsistency across sections that were reviewed hours apart.
 
Worth the additional stage for customer-facing product documentation, multilingual releases going live at the same time, and long technical documents that several people will read closely