Artificial Intelligence

Health AI

Clinical voice notes & decision support.

We co-design with clinicians. Models are evaluated on local disease patterns, shipped on-prem where needed, and wrapped with audit trails that match national health-data regulation.

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29pts

WER reduction on clinical audio

4 hospitals

Active deployments

0 bytes

Patient data leaving the facility

65%

Clinician note time saved

What we ship

Capabilities

Voice-to-EMR transcription, triage assistants and clinical decision-support models built with Tanzanian hospitals — privacy-preserving and on-soil.

01
Swahili clinical ASR & summarization
Doctor-patient consultations transcribed and structured into SOAP notes — 29-point WER reduction on code-switched clinical audio in our field study.
02
Triage & symptom-routing copilots
Decision-support models trained on East African disease prevalence — malaria, TB, hypertension — with referral logic aligned to national clinical guidelines.
03
On-prem deployments inside hospitals
Air-gapped or hospital-network deployments where patient audio and data never leaves the facility — required by Tanzanian health data regulation.
04
Audit logs aligned with health regs
Immutable inference logs, consent tracking and data-retention controls that satisfy MOHCDGEC and PDPA requirements.

Outcomes

Free up clinician time from note-taking
Structured EMR entries from free-form speech
Compliant, sovereign deployments

Tech we use

WhisperLLaMA / MistralFHIRPostgresKubernetes

In the field

1
Muhimbili cardiology ward
Live transcription of doctor-patient consultations — WER 12%, structured SOAP notes in under 10 seconds.
2
Rural health centre triage
Symptom-routing copilot running on a Raspberry Pi at facilities with no reliable internet — referral accuracy 89%.
3
Federated imaging across 4 hospitals
TB chest X-ray classification across Tanzania, Kenya and Uganda — no patient images shared across borders.

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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
Clinical co-design
We embed with clinicians for at least two weeks before touching a model — understanding the real workflow, not the textbook one.
Step02
Consented data collection
Audio collected under IRB-approved consent flows, de-identified and retained per MOHCDGEC data governance rules.
Step03
Model fine-tuning & clinical review
Clinicians review model output at every evaluation checkpoint — F1 on clinical terms is the metric, not generic WER.
Step04
On-prem deployment & handoff
Kubernetes or bare-metal deployment inside the hospital network, with a documented runbook for the IT team.