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.
A recording or document with the terminology that usually gets mangled.
This is for you if
If none of these are true we are probably not the right call yet, and we will say so.
- 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
What a language cell is judged on.
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.
Domain vocabulary, place names, unit designations and jargon handled correctly, because the model is taught them rather than guessing from general training data.
Transcription and translation volume that previously waited on scarce bilingual staff, processed continuously instead.
Where it runs, and on what
Ask the harder questions.
The four objections that decide whether a language capability is usable.
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
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
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
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
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
What Bhaasha does
Speech recognition across Indic languages and English, including regional accents and noisy recordings.
Translation that holds your terminology, rather than smoothing it into something generic.
A path to correct the model on your own vocabulary — the missing piece in every off-the-shelf option.
One index across source and translated material, so a query in one language finds the other.
Runs on your hardware inside your network, with no external calls in the processing path.
Built for perimeters where sending material to a public service is not an available option.
How it is deployed.
Inside your perimeter, sized to the queue you actually have.
Installed on your hardware inside the security perimeter, with no outbound calls during inference.
Bulk backlogs and live streams handled by the same deployment, fitting the workflow the cell already runs.
Low-confidence segments route to a linguist rather than being published, and their corrections are retained.
Built and maintained by Idea Infotech, with support delivered inside the security perimeter.
Talk to us about the terminology that gets mangled.
Tell us on a call which terms general-purpose tools keep getting wrong in your domain, and what the material sounds like. Nothing has to leave your side for us to have that conversation.
Bhaasha — common questions
Indic languages alongside English, including material where two scripts appear in the same document or where a speaker switches language mid-sentence. Coverage is extended per deployment rather than fixed, because the languages that matter vary by customer.
Two ways, and both are structural. Your material never leaves the perimeter, so it can be used where a public API is simply not permitted. And it can be taught your terminology — place names, unit designations, domain jargon — where a general-purpose service will keep smoothing them into something plausible but wrong.
Yes, and that is the point of the tuning path. Corrections feed back into the domain vocabulary so the same mistake is not repeated — which is the capability most off-the-shelf transcription products do not offer at all.