A generic translation model will quietly mangle the words that matter most in your meeting — your product names, your internal acronyms, your domain jargon — because it treats them as ordinary language to be translated rather than fixed terms to be preserved. The fix is a glossary: you pin how each key term should render, and the same term comes out the same way every time, in the live captions and in the transcript that follows.
How a generic model breaks names, acronyms, and jargon
A translation model's job is to turn meaning in one language into meaning in another, and that instinct is exactly what trips it up on the terms you can't afford to lose. It sees your product name and, not knowing it's a name, translates it literally — a brand that's an ordinary word in English becomes that ordinary word in Japanese, and now nobody in the room recognizes the thing being discussed. It hears an acronym and expands it to the wrong phrase, because three letters can stand for a dozen things and the model guesses from general usage rather than from how your team uses it. And it renders the same piece of jargon three different ways across one call, because each time it re-translates from scratch with no memory that it already chose a word ten minutes ago.
None of these are dramatic failures. That's what makes them dangerous. The English stays fluent, the captions keep flowing, and the error only surfaces later when someone reads the transcript and can't tell whether two differently-translated terms are the same thing or two different things.
What a glossary actually does
A glossary is a short list of your terms and how each one should be handled. For a product name or a person's name, the instruction is usually "leave this alone" — carry it through untouched in every language. For an acronym, you pin the expansion you mean, or keep the acronym as-is. For domain jargon, you fix the one rendering you want, so the term stops drifting.
The point isn't to translate more — it's to translate less where translating is the wrong move. A glossary tells the model which words are off-limits and which have a single correct form, and applies that consistently for the whole meeting, so the parts of the conversation that carry the most weight come through recognizable instead of being silently reinterpreted.
Consistency across live captions and the transcript
The half of this that teams miss: a term needs to render the same way in two places, not one. During the call, people read live captions in real time. After the call, the same conversation becomes a written transcript and a summary that someone who missed the meeting reads cold. If the captions call your platform one thing and the transcript calls it another, the record stops matching what people saw — and the reader who wasn't there can't reconcile the two.
A glossary that applies to the whole pipeline fixes both at once: the same pinned term flows through the live translation and into the written record, so the transcript is a faithful copy of the meeting, not a second, slightly-different translation. Fixing it once at the source beats re-keying terms across every meeting your team runs.
How to build a good term list
You don't need a huge glossary; you need the right twenty or thirty entries. Start with the terms that, if rendered wrong, would actually confuse someone:
- Product and feature names — anything a generic model would translate as an ordinary word. Mark these "do not translate."
- Company and team names — yours, your customers', your partners'. Names are not words; carry them through.
- Internal acronyms — the ones specific to your company or industry, where the expansion isn't obvious. Pin the meaning you intend.
- Domain jargon — the handful of technical or business terms your meetings lean on, where you want one consistent rendering rather than three guesses.
- People's names — especially names that double as common words in some language.
Keep it lean and let it grow. Add a term the first time you watch it come out wrong; don't try to anticipate everything on day one. A good glossary is a living list of the words your team actually uses, not a dictionary.
One honest caveat: a glossary fixes terms. It is not a switch that makes the whole translation perfect — grammar, nuance, and accent still depend on how well the tool handles your languages overall. It solves the specific, recurring, infuriating problem of your own vocabulary getting mangled, and it solves it well. For the broader question of what "accurate" even means here, see how accurate is AI meeting translation, and for choosing a tool in the first place, what to look for in a meeting translation tool.
How to do it with Sageio
- Add
bot@sageio.netto your Google Meet calendar invite. It joins on its own — no extension, nothing to install. - Set up a personal glossary on the Professional plan. List your product names, acronyms, and jargon with how each should render. The pinned terms apply across the meeting.
- Each participant picks their caption language. Everyone reads the meeting in their own language — Sageio translates into 20+ languages — and your glossary terms come through consistently for all of them.
- Everyone speaks naturally. Translated captions appear in about two seconds, with your key terms held to their pinned form.
- Afterward, a searchable transcript and an AI summary arrive within about five minutes — using the same glossary, so the written record matches the live captions.
(Today this runs on Google Meet; Zoom and Microsoft Teams support is coming soon.)
Is it private?
For anything that joins your meetings: Sageio doesn't use your meeting content to train AI models, and its AI vendors are contractually restricted from doing the same. Audio is processed in memory and discarded — only the text transcript and summary are kept, encrypted, in the region you choose (US, EU, or APAC). Enterprise customers can self-host the entire stack.
Frequently asked questions
Why does a translation tool get my product names wrong? Because a generic model treats a name as a word to translate, not a fixed term to preserve. If your brand or feature name is also an ordinary word, the model translates it literally and the name becomes unrecognizable in the target language. A glossary tells it to leave those terms alone.
What's the difference between a glossary and just better translation? A glossary fixes terms — names, acronyms, jargon — so they render the same way every time. It doesn't fix grammar or nuance; those depend on how well the tool handles your languages overall. The two are complementary: a glossary solves the recurring vocabulary problem that even a strong general model keeps getting wrong.
Does the glossary apply to the transcript too, or only live captions? Both. The same pinned terms flow through the live captions and into the searchable transcript and summary, so the written record matches what people read during the call instead of being a second, slightly-different translation.
Which plan includes a glossary? Sageio's Professional plan includes a personal glossary, with higher term limits on Teams and Enterprise.
What does it cost to try? Every plan starts with a free 60-minute trial, no credit card required. After that, Professional is $49/month and Teams is $99 per seat/month (annual billing includes 2 months free); Enterprise is custom-priced.
Your product names and acronyms are the words your meeting can least afford to lose, and they're exactly the ones a generic model mangles. Pin them once in a glossary and they come out right every time — live and in the record. Add the bot to one call and watch your own terms hold their shape.