Renolab

AI-powered early detection and monitoring of kidney disease

The Problem

Kidney disease is often detected late in Africa due to limited diagnostic tools, lack of awareness, and fragmented healthcare systems. This leads to high mortality rates and preventable complications.

The Solution

Renolab leverages machine learning to analyze patient data and detect early signs of kidney dysfunction, enabling proactive care and improved patient outcomes.

Our Journey

Prof PK Mante

Mentorship & Foundation

Renolab was built under the mentorship of Prof. PK Mante, whose guidance shaped our clinical and research direction. Alongside my co-founder Solomon, we set out to tackle early detection of kidney disease using data-driven approaches.

A2SV Hackathon

A2SV Hackathon – Quarterfinalist

Our first major test was the A2SV Hackathon, where we competed among thousands of participants across Africa and reached the quarterfinals. This validated both our idea and our execution capabilities.

Mastercard Pitch

Mastercard Foundation SBS Grant

We pitched Renolab and secured seed funding through the Mastercard Foundation SBS program, enabling us to move from concept to real-world development and validation.

CICSI Nairobi

CICSI Africa Division – Bronze Medal

At the CICSI African Division Finals in Nairobi, Renolab was awarded a Bronze Medal, marking a major milestone in our journey and recognition of our innovation on an international stage.

Key Features

Predictive Modeling

AI models for early disease detection

Patient Monitoring

Track progression and risk factors

Clinical Insights

Actionable recommendations for clinicians

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