DeepL is a powerful solution at the segment level, and its output can certainly be improved with rigorous glossary management. However, premium translation requires a more nuanced approach. Automation without manual, white-glove management breaks down at multiple points in the process.
I run the GAIT-Augmented MT & MTPE workflow manually using generative AI, rather than conventional machine translation engines. This allows me to process large blocks of text at once to capture the necessary context, while still maintaining the structural advantages of working within a CAT tool. Conventional automated workflows simply cannot do both.
Because my process isn’t constrained by rigid, sentence-by-sentence segmentation, the AI leverages broader context to deliver better results. I actively prompt the AI to match the style, mechanics, and terminology of your specific resources. This produces more fluent translations and significantly reduces the common machine translation issue of rendering a single term or phrase multiple ways within the same document. Furthermore, manual management gives us the flexibility to iterate and refine the AI’s output as longer projects progress.
Achieving this standard also requires looking beyond the AI itself. I perform thorough file preparation before the text ever touches an engine. Correcting fragmented segmentation, OCR errors, “tag soup,” and broken formatting—especially on messy PDF-to-Word conversions—eliminates factors cause automated engines to produce errors.
Finally, unlike dropping whole files into DeepL, my process keeps everything securely integrated within a CAT tool environment. This lets us build your translation memories, enforce terminology, and run comprehensive QA checks to verify accuracy before the final handoff.
If you are curious about the difference, let’s do a side-by-side comparison. You run a sample text through DeepL, and I will run it through my process. Have your trusted linguist compare the output—their feedback is the only test that matters.
