The devil really is in the details—and today, you can take a peek at some samples from the Logrus IT AI tools kitchen.
In my recent episodes on AI in translation, I mentioned that the idea of "pure human" translation has largely become an illusion today. At Logrus IT, we’ve moved toward the PRISM process (Pre-Translation, Raw MT, AI-based Improvement, human Specialist review, and Metric-based, hybrid QA).
But nice acronyms only go so far. Let’s look at some tangible proof of how our AI-based Quality Assurance (AiLQA) actually works on real projects. Special thanks Fedir B. for collecting and providing samples!
The Midnight Oil & The Near-Flop. Picture a super-urgent overnight translation with zero time for traditional editing/proofing. One deck featured slides with employee photos and names. Two contributors had the same last names (!) and also similar first names: Vera and Vicky. Only "Vera" was already in the TM. The tiny 3-letter difference resulted in a high fuzzy TM match that the exhausted translator missed.
Traditional automated QA wouldn't have caught this relationship killer. AiLQA saved the day: it didn't just flag the mistranslation, it suggested replacing "Vicky" (a diminutive) with her full name, Victoria.
The "Adequate Translation" Trap. Turning a senseless source text into an equally senseless target without overthinking it is an old tradition. Take this string from a hardware manual: "Tools: multimeter (measurable secondary pipe)." Originally it was translated as "measurement on the secondary side of the circuit." Obvious engineering nonsense!
Our AiLQA tool flagged it by analyzing the broader context of testing rectifier bridges. It spotted a classic "Chinglish" artifact: that "secondary pipe" was a literal, erroneous translation of the Chinese word for "diode" (二极管). The tool explained the root cause of the error and provided a flawless, technically accurate suggestion: "Tools: multimeter (with a diode testing function)" and an equally flawless translation.
This is a paradigm shift in localization quality assurance. Our AI tool demonstrated a deep understanding of both context and subject matter area, bridging the gap between flawed source material and perfect localization.
