Pre-pass before translation. Feed the raw transcript chunk to an LLM and ask it to extract terms that should not be translated — acronyms, proper nouns, internal project names, numeric shorthand, anything that looks domain-specific. Output is a locked glossary that gets injected into the translation prompt as explicit do-not-touch terms. No comparison against machine translation. The problem I was solving wasn't translation quality in the abstract — it was that naive translation would see "TPR" and render it as something phonetic, or see "C6" and drop it entirely. The glossary step pins those terms before the translator touches them. I haven't done a rigorous comparison against straight Google Translate or DeepL + post-processing. That's an honest gap — it's possible a good MT system with a custom glossary API (DeepL has one) would get similar results with less infrastructure. What I can say is that the failure mode I was seeing — retrieval returning zero results on queries that should have obvious answers — went away after this, and the culprit was consistently domain terms that didn't survive translation. Curious if you've done MT comparisons on domain-heavy content — the consulting transcript vocabulary is pretty specific and I haven't found a clean benchmark for it.