Artificial Intelligence

Voice & Speech AI

ASR, TTS and voice agents tuned for the region.

From 8 kHz phone audio in rural clinics to Sheng-heavy urban conversations, our voice stack is trained on the real acoustic conditions your users live in. We ship both self-hosted and managed deployments.

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12%

WER on real ward recordings

3,400 hrs

Training audio corpus

<500 ms

Latency on mobile networks

4 dialects

Regional coverage

What we ship

Capabilities

Speech recognition and synthesis trained on real Tanzanian and Kenyan voices, dialects and call-quality audio — production-ready for IVR, clinics and field ops.

01
Swahili & Kiswahili-Sheng ASR
Acoustic models trained on 3,400+ hours of real East African speech — ward recordings, call-centre audio, field devices. WER down to 12% on clinical audio.
02
Natural-sounding Swahili TTS voices
Studio-quality synthesis in multiple regional voices and dialects, fine-tuned on 100+ hours per persona for IVR, accessibility and brand applications.
03
Voice agents over telephony & WhatsApp
End-to-end voice bots wired to Asterisk, Twilio or Africa's Talking — with barge-in, turn-taking and sub-second response on real mobile networks.
04
Diarization for multi-speaker recordings
Speaker separation for clinical consultations, legal proceedings and call-centre QA — with language-ID for code-switched audio.

Outcomes

Voice agents that work on real African phone networks
Accurate transcripts for clinics, courts and call centres
Natural Swahili voices for brand applications

Tech we use

WhisperNeMoCoquiAsteriskTwilio

In the field

1
Hospital ward transcription
Muhimbili National Hospital — doctor-patient consultations transcribed in real time, structured into EMR fields.
2
Rural IVR for mobile money
Voice-driven M-Pesa support in Swahili and Sukuma for users who cannot read SMS prompts.
3
Court reporting automation
Verbatim Swahili transcription of proceedings, with speaker diarization and legal-term glossary.

Discuss your use case

How we deliver

Our delivery process

Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.

Step01
Acoustic environment audit
We record and profile your real audio conditions — phone codecs, ambient noise, code-switching frequency — before touching a model.
Step02
Domain fine-tuning
Whisper or NeMo base fine-tuned on your vocabulary: medical terms, product names, regulatory language.
Step03
Integration & latency testing
We wire the model into your telephony stack and test end-to-end latency on real African network conditions.
Step04
Monitoring & accent drift
Ongoing WER monitoring with automatic retraining triggers when new speaker demographics emerge.