Five languages into English, on servers that never go online.

The outcome.

Analysts on the programme now read message traffic in five languages in English, at more than 2,000 words a minute.

It runs on the servers each site already had. Nothing leaves the site. And the site improves its own models, without waiting on us.

The problem.

A systems integrator was delivering a multi-source analysis platform for India's Ministry of Defence, at several secure, air-gapped sites.

The analysts work in English. The traffic arrives in Chinese, Persian, Dari, Pashto and Arabic, in large volumes, and it is short, clipped and abbreviated.

Cloud translation was ruled out from day one: nothing may leave the perimeter. The programme required 2,000 translated words a minute, and when the work began, not one site had a GPU.

What we built.

Our own translation models, trained for this traffic, with one model per language so a weak language can be improved without affecting the others.

Every translation used to train them was checked by native-speaker linguists first. We then tuned each model against a fixed benchmark until it met the speed target on the hardware the sites already had.

Built for the people who run it.

Analysts keep glossaries in the console. A code name that translates badly is fixed in seconds and applied on the next request.

A point-and-click training tool lets the site retrain any language on new data, inside the perimeter, with no engineer present. It scores the new model against the old one before anything is adopted.

What changed.

The programme got the translation it needed without new hardware, and without giving up control of it.

• Live on the existing servers, instead of waiting on a hardware procurement cycle.
• One release for every site. Where a site later added a GPU, Bhaasha uses it automatically.
• Nothing leaves the site: no external API, licence check or telemetry.
• The speed figure is reproducible on the site's own hardware, with the benchmark tool that ships with the system.
• Model quality no longer waits on us. The site improves its own models on its own schedule.

Where it is now.

Bhaasha was delivered in 2024–2025 and runs across the platform's air-gapped sites.