Material that never leaves
The whole suite runs inside your perimeter, so sensitive audio and documents are processed where they already sit rather than being sent to someone else's API.
It never leaves the building.
Bhaasha transcribes and translates across Indic languages and English inside your own perimeter — and can be taught the terminology your work actually depends on.
No public API in the path. Ever.
On-premise
Air-gap capable
Indic & English
Languages
OEM
Government-certified
Inside the perimeter
Taught your terminology.
Transcription and translation across Indic languages and English, with no public API in the path.
Where it runs, and on what
Ask the harder questions.
The four objections that decide whether a language capability is usable.
Detailed view
A public API is not an option here.
For most of the material Bhaasha handles, sending it to a hosted endpoint ends the conversation. The suite installs on-premise inside the security perimeter and runs with no outbound path, which is what makes the use case possible at all.
- Deployed inside your perimeter
- No outbound calls during inference
- Runs air-gapped where required
- Your hardware, your retention policy
- No third-party training on your material
- Auditable by your own security team
Detailed view
Generic models smooth away the words that matter.
Off-the-shelf translation is fluent and wrong in exactly the places that count — unit names, ranks, statutes, procedures. Bhaasha is tuned on your glossary so domain terms survive translation instead of being paraphrased into something plausible.
- Tuned to your glossary and register
- Domain terms held, not paraphrased
- Named entities preserved across languages
- Corrections fed back into the model
- Consistency across a document set
- Terminology reviewed by your own linguists
Detailed view
Recordings are not studio clean.
Field audio has background noise, regional accent, overlapping speakers and mid-sentence switching between an Indic language and English. Recognition is built against that material rather than against clean read speech.
- Indic languages and English
- Regional accents and dialects
- Noisy and low-bitrate recordings
- Code-switching handled mid-sentence
- Speaker separation on overlapping audio
- Timestamps aligned to the source
Detailed view
Built into how the cell already works.
A language capability only helps if it fits the existing queue. Batch and streaming modes, confidence scores that route uncertain output to a human, and formats that drop into the tooling already in use.
- Batch and real-time modes
- Confidence scores per segment
- Low-confidence output routed to a linguist
- Human corrections retained and reused
- Exports into existing reporting formats
- Throughput sized to your queue
Detailed view
A public API is not an option here.
For most of the material Bhaasha handles, sending it to a hosted endpoint ends the conversation. The suite installs on-premise inside the security perimeter and runs with no outbound path, which is what makes the use case possible at all.
- Deployed inside your perimeter
- No outbound calls during inference
- Runs air-gapped where required
- Your hardware, your retention policy
- No third-party training on your material
- Auditable by your own security team
This is for you
This is for you if
- Generic models mistranslate the terminology your work depends on
- A public API is the only option and the material cannot leave
- Transcription is still being done by hand, at volume
- Multilingual material loses meaning by the time it is searchable
If none of these are true we are probably not the right call yet, and we will say so. Tell us which terms general-purpose tools keep getting wrong in your domain, and what the material sounds like. Nothing has to leave your side for that conversation.
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