We do not sell generic AI. We bring the same research rigour, local context understanding, and explainability-first approach from our flagship HIV tool to every engagement we take on.
Most organisations waste 12–18 months and significant budget on AI projects that fail because the problem was wrong, the data was not ready, or the team did not know what good looked like. We help you avoid that.
We have built a live, ethics-approved AI tool in one of the most data-constrained, resource-limited environments in the world. That experience is what you are buying when you bring us in as consultants — not generic frameworks, but hard-won practical knowledge.
From AI readiness assessment to full deployment roadmap — we give you the honest picture of what AI can and cannot do for your specific context, data, and goals.
Generic AI models trained on Western data sets will not work in your context. We build models from the ground up — or fine-tune existing ones — on your specific domain data, with explainability built in from day one.
Our speciality is Explainable AI for high-stakes decisions: healthcare, agriculture, finance, and social services. Every model we build can show its reasoning — because we believe no AI system should make important decisions about people's lives without being able to explain itself.
We specialise in models for healthcare, agriculture, and social impact — trained on local data, built for local constraints, and designed to explain every prediction they make.
We have seen what bad data does to AI projects. We spent months cleaning, anonymising, and structuring 20,604 patient records before our model ever ran for the first time. That experience is the foundation of every data engagement we take on.
AI is only as trustworthy as the data it was trained on. We build the pipelines, governance frameworks, and annotation workflows that make your data ready for AI — and keep it that way.
From raw clinical records to AI-ready datasets — we build the data pipelines, governance frameworks, and annotation systems that make reliable AI possible.
One of the reasons our clinical decision support tool was designed to integrate with existing health information systems is because we know what happens when technology demands a full system replacement: it gets abandoned. We build AI that slots into your existing workflows.
Whether you need a REST API, a microservice, or a full embedded AI feature inside your existing software — we design the integration around your infrastructure, not the other way around.
AI that demands a full system replacement gets abandoned. We build integrations that work within your existing infrastructure — REST APIs, embedded models, and microservices designed for your environment.
The biggest risk in any AI project is that the organisation becomes dependent on the vendor and cannot maintain the system themselves. Every training programme we deliver is designed to build genuine internal capability — not dependency.
We train everyone from clinicians who need to understand AI outputs to data scientists who want to build the next generation of health AI in Africa.
We train clinicians, executives, and data scientists to understand, use, and build AI responsibly — building the internal capability that makes AI projects succeed long after the vendor has left.
Most AI vendors disappear after deployment. Patient populations change. Data distributions shift. Models that were accurate 18 months ago start degrading. Without ongoing support, AI systems become liabilities.
We provide dedicated technical support that keeps your AI systems accurate, performant, and trusted — with transparent health reports that explain exactly how the model is performing against the metrics that matter.
AI systems degrade without ongoing attention. We provide the monitoring, retraining, and transparent reporting that keeps your AI systems performing as well on month 18 as they did on day one.
Every engagement follows the same four-step process — built on honesty, rigour, and the lessons of building a live AI tool from scratch with zero funding.
We start by deeply understanding your challenge, data landscape, clinical context, and goals — before we recommend anything.
We architect the right AI solution for your specific situation — balancing performance, explainability, cost, and feasibility.
We develop, train, and rigorously evaluate your AI system — with transparency about what it can and cannot do.
We deploy with full documentation, staff training, and ongoing monitoring — because launch is the beginning, not the end.
We are not a consulting firm that learned about Africa from a report. We are researchers who built a working AI tool in Uganda — with all the constraints, complexity, and context that entails.
Built at Makerere University, based in Kampala, trained on Ugandan patient data. We understand your context because it is our context.
We will never deliver an AI system that cannot explain its decisions. Every model we build is designed to show its reasoning — because we believe that is the only ethical approach.
Everything we build is backed by peer-reviewed methodology. We do not cut corners on validation, ethics, or documentation.
We will tell you when AI is not the right solution. We will tell you when your data is not ready. That honesty is the foundation of lasting partnerships.
We actively seek NGO, government, and academic partnerships. We are not trying to extract value — we are trying to build something that lasts.
Low bandwidth. Limited compute. Small datasets. We have built in the hardest conditions. That means we know how to build AI that actually works in African contexts.
Tell us about your challenge. We will give you an honest assessment of what AI can do for your specific context — and what it cannot. No hype. No generic decks. Just the right answer.