Kujali provides an AI‑driven clinical decision‑support platform that analyzes real‑time labor data to generate risk scores and evidence‑based care recommendations for obstetricians, midwives, and nurses. Designed for low‑resource settings, the offline‑first system runs on modest hardware and highlights high‑risk situations to help reduce preventable maternal deaths and unnecessary interventions.
Funding
Funding not disclosed
Founders
Product
Problem
In low- and middle-income countries, labor and delivery providers often lack timely, evidence‑based guidance, leading to high maternal mortality and the overuse of interventions such as unnecessary cesarean sections.
Solution
Kujali delivers an AI‑driven clinical decision‑support platform that processes real‑time patient data during labor to generate actionable recommendations for frontline health workers. The system integrates obstetric risk models developed with global medical experts and runs on low‑resource hardware, enabling use in clinics without reliable internet connectivity. By highlighting critical risk factors and suggesting evidence‑based interventions, Kujali aims to reduce preventable maternal deaths and avoid unnecessary procedures. Ongoing pilots with partners such as Jhpiego and regional hospitals validate the algorithms on data from over 2,000 patients, with the goal of scaling impact to millions of births.
Target Audience
Primary users are obstetricians, midwives, and nurses delivering care in public hospitals, community health centers, and mobile clinics across low‑ and middle‑income regions.
Features
- Real‑time analysis of vital signs, labor progress, and patient history to produce risk scores and care suggestions
- Offline‑first architecture that syncs data when connectivity is available, suitable for resource‑constrained settings
- Clinician‑focused alerts that prioritize high‑risk situations while minimizing false positives
- Dashboard visualizations that display key maternal health metrics and recommended actions at the point of care
- Continuous learning loop that incorporates feedback from providers to refine AI models