Conventional machine translation is an automated, static process that produces disconnected text, turning premium translators into mechanics forced to clean up a machine’s mess. While better engine customization is possible, boutique LSPs rarely have the dedicated backend engineering staff required to pull it off.
My approach bridges this gap. I use a highly curated, iterative workflow that removes the mechanical friction entirely and delivers contextually fluent drafts right out of the gate. The result is a fundamentally higher-quality baseline that reads naturally and is significantly easier—and much less frustrating—to post-edit.
The biggest differentiator is how I eliminate the “Frankenstein Effect.” Conventional MT translates segment-by-segment with little-to-no context, independently mixing exact TM matches, fuzzy matches, and raw MT. This forces the translator to stitch a disjointed text together. I translate in broad, context-aware blocks across as few context windows as possible. By feeding TM hits and a “gold standard” sample—even the translator’s own work, if possible—directly into an anchor prompt that holds the project together even when changing context windows, the entire document is generated in a cohesive, fluent voice.
Next, I solve the formatting and tag issues that plague cheap MT. Where automated systems break segments, lose formatting, and create frustrating “tag soup,” I take the time to manually prepare the source text. I correct segmenting, strip unnecessary tags, lock out high-value internal fuzzies, and maintain formatting before the translator ever touches the file.
Furthermore, my process is dynamic and iterative, whereas standard MT is completely static. As a large project progresses, I actively tweak and improve the AI prompt based on ongoing feedback to eliminate structural problems and capture new terminology as we go.
Ultimately, this elevates the translator experience. Instead of acting as a “janitor” fixing mechanical glitches, your linguist is empowered to act as a strategic editor. Because the MT is tailored to their style and stripped of irritating mechanical roadblocks, their cognitive load drops. They gain a sense of ownership over the iterative process, allowing them to work faster, happier, and focus on higher-level quality.
You get a smoother workflow, your translators get a better working experience, and the end client receives a superior final product.
