Sada
From speech to clear decisions
We understand deeply. We build from the source. And we run with confidence.
We understand Arabic as people speak it.
From understanding speech, sounds and dialects to generating natural, accurate voice.
Sada runs multi-agent analysis over the transcript, so a meeting ends with a structured record rather than a wall of text.
Speech recognition trained for Arabic rather than adapted to it, so the transcript is usable input for search and for analysis.
Sawt Najd is a Najdi TTS model trained on Najdi broadcast speech, a real Saudi voice, not an MSA approximation.
Sadeed restores Tashkeel so a TTS system reads unambiguous text instead of guessing the word.
What changes once what was said can be found.
Calls and meetings join the rest of the organisation's searchable record instead of piling up as unprocessed audio.
Decisions, actions and risks are written down as structure, which is the part anyone actually needs afterwards.
The recordings an organisation is least able to send to a cloud service are the ones this handles on-premises.
Two directions, one stack: Sada turns speech into text — dialect-aware Arabic transcription that ends a meeting with a structured record of decisions, actions and risks, on your premises. Sawt Najd turns text into speech — a Saudi voice trained on broadcast Najdi, natural and precise rather than a Fusha imitation.
From speech to clear decisions
On-premises for the audio that cannot leave, the cloud API for the rest, or dedicated in-Kingdom hosting in between.
Sada runs meeting intelligence entirely inside your environment.
Speech models through Kawn Console for teams building voice features into their own applications.
Hosted inside Saudi Arabia where residency is required but running the hardware is not practical.
“Manual review effort was reduced as extraction moved from manual re-keying to automated structured output.”
A national statistics authority · Customer case study
Read the case studySada turns meeting audio into decisions, actions and risks on-premises, understanding both directions of the voice channel - transcription and generation - in one place. Our speech research also produced Nabr, a Quranic recitation model analysing 8 acoustic properties per sound at 0.59% character error rate - the level our acoustic modelling operates at.
Understanding and generating audio are one procurement for a contact centre, one solution, not two listings.
Our acoustic modelling is trained on Arabic sound, not fine-tuned from an English model, what makes dialect tractable.
Board minutes and HR investigations are exactly what a cloud-only tool cannot take, why Sada deploys on-premises.
Tell us the problem. We will tell you honestly whether this is the right answer, and what it takes to deploy.