Most tools mishandle Kannada for a reason their architecture was never built to face: Kannada is Dravidian and agglutinative, so a single verb can carry tense, person, and mood as stacked suffixes — what English needs a whole clause to say. Add the gap between spoken Kannada and the formal written register models train on, and "supports Kannada" on a feature list tells you very little. But the harder problem is specific to Bengaluru: tech offices speak Kanglish — Kannada and English mixed in one breath — and the room isn't only Kannada speakers. A typical Bengaluru standup has Kannada, Hindi, Tamil, and Telugu colleagues on the same call, which quietly breaks any tool that assumes one language per meeting. Here's what actually decides whether a Kannada meeting comes back usable.
One verb can be a whole clause
Kannada is agglutinative: it builds meaning by stacking suffixes onto a root rather than spreading it across separate words. Take māḍu ("do"). Māḍide is "I did"; māḍabēku is "must do / have to do"; māḍuttēne is "I am doing / I do." The same root absorbs tense, person, and modality into one written-as-one-word form, so where English uses three or four words, Kannada uses one. A recognizer that segments on the assumption that words map roughly one-to-one will either split a verb in the wrong place or drop the suffix that carried the modality — and "must do" quietly becomes "do" in the transcript. That dropped layer is exactly the part a summary later depends on.
Spoken Kannada isn't written Kannada
Kannada has a strong diglossia: the formal written register (the Kannada of newspapers, documents, and textbooks) differs from the colloquial register people actually speak, in vocabulary, in verb endings, and in how words contract. A model trained mostly on written text has heard the formal forms far more than the spoken ones, so it mishears the everyday speech of a real meeting — the casual contractions and conversational endings that never appear in print. The result is a transcript that's technically Kannada but doesn't match how anyone in the room actually talked.
Bengaluru meetings are Kanglish — and not everyone in the room speaks Kannada
In a Bengaluru tech office, professional Kannada is Kanglish: Kannada grammar with English technical nouns and verbs dropped straight in. "Ī feature anna next sprint alli deploy māḍbēku" is one normal sentence — English content words, Kannada frame and verb. A tool that detects "Kannada" may leave the English untranslated; one that detects "English" leaves the Kannada. And the deeper issue is who's listening: Bengaluru is a migrant tech hub, so the same standup carries Kannada, Hindi, Tamil, and Telugu speakers plus a remote teammate on English. One-language-per-room is the wrong model entirely — each person needs the meeting rebuilt in their own language, which is the whole point of per-person captions.
Why "supports Kannada" isn't enough
A tool can list Kannada, transcribe a clean dictionary sentence, and still fall apart on the stacked suffixes, the spoken-vs-written gap, the Kanglish, and a room where four languages share one call. The feature list won't tell you which. One real meeting will: does a Kannada speaker read the captions and a Tamil colleague read theirs and both recognize how the room actually talked? For why this pattern repeats across Asian languages, see real-time translation for remote teams.
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. - Each participant picks their caption language. A Kannada speaker reads Kannada, Hindi/Tamil/Telugu colleagues each read their own, a remote teammate reads English — all from the same spoken meeting. (Sageio translates into 20+ languages.)
- Everyone speaks naturally — stacked verbs, Kanglish, all of it. Translated captions appear in about two seconds.
- Afterward, a searchable transcript and an AI summary arrive within about five minutes, shared at the host's discretion.
(Today this runs on Google Meet; Zoom and Microsoft Teams support is coming soon.)
How to test any tool in five minutes
Say a long agglutinated verb in a sentence — something built on māḍu where the modal suffix matters, like māḍabēku ("must do") versus plain māḍu ("do") — and check whether the captions keep the "must," not just the "do." Then say a normal Kanglish line ("ī bug anna today fix māḍbēku" — "we have to fix this bug today") and see whether it keeps the English words whole while rendering the Kannada correctly. If the suffix gets dropped or the English comes back garbled, the tool wasn't built for spoken Kannada.
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 agglutination break transcription? Kannada stacks tense, person, and mood as suffixes on one verb root — māḍabēku ("must do") is a single word carrying meaning English spreads across a clause. Tools that segment as if words map one-to-one can split the verb wrong or drop the suffix that held the modality, so "must do" silently becomes "do" in the transcript.
What is the spoken-vs-written gap in Kannada? Kannada is diglossic: the formal written register differs from everyday spoken Kannada in vocabulary and verb endings. Models trained mostly on written text have heard far more of the formal forms, so they mishear the casual contractions of real speech — and a meeting is all speech.
Why do Bengaluru meetings need per-person captions? Bengaluru is a migrant tech hub, so one standup often mixes Kannada, Hindi, Tamil, and Telugu speakers plus a remote teammate on English. A one-language-per-room assumption fails; each participant picks their own caption language and reads the same meeting in it.
How fast are the translated captions? About two seconds, fast enough to keep a live conversation moving, with a searchable transcript and summary within about five minutes after the call.
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.
If your team works in Kannada, the honest test is whether a Kannada speaker reads the live captions and transcript and hears the actual meeting — the modal suffix kept, the spoken register matched, the Kanglish whole — while the Tamil and Telugu colleagues next to them read the same call in their own language. Add the bot to your next standup and let the room judge.