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AI & Data Science

Build ML/AI solutions that use prediction, classification, forecasting, optimization, computer vision, or anomaly detection to solve Tanzania's sector challenges. Chatbot / conversational-assistant projects will not be accepted.

Areas of Focus

Agriculture & Food Security

An ML/AI solution that helps small farmers maximize their production, get good market prices, and solve the wider range of problems affecting them - from crop pests and disease, to unreliable yields, to lack of access to credit, insurance, and market information.

Legal & Justice Sector

An ML/AI solution that helps courts, legal aid providers, and citizens deal with case backlogs, slow dispute resolution, unequal access to justice, and other related challenges.

Public Health

An ML/AI solution that helps health facilities and patients get better, faster, and fairer care. The goal is to solve the full range of problems that make health service delivery inefficient in Tanzania - not just one narrow symptom of it.

Revenue & Public Finance

An ML/AI solution that helps government expand the tax base - especially in the informal sector - make compliance simpler for small businesses, and solve the broader problems around revenue leakage and inefficient audits.

Transport & Urban Infrastructure

An ML/AI solution that helps big cities like Dar es Salaam move people and goods more efficiently, reduce accidents, and solve the wider problems around congestion, poor scheduling, and failing infrastructure.

Water Resources & Climate Resilience

An ML/AI solution that helps district water authorities and communities avoid water-point failures, prepare for drought, and solve the broader problems tied to water scarcity and flooding.

What to Submit
Choose one focus area and submit a short concept brief (1–2 pages) covering: • The Problem - What specific challenge are you solving, and which institution or group faces it? • Who It Affects - Who is the end-user or decision-maker who benefits? • The Data - What data would be needed, and where might it come from? • The Approach - What kind of ML/AI method fits? (prediction, classification, forecasting, optimization, computer vision, anomaly detection) • What Success Looks Like - How would you know the solution is working? Note: Chatbot / conversational-assistant projects will NOT be accepted. We are looking for solutions built on structured ML/AI techniques.
Ready to Compete?

Register for RIDC PT Innovation Hackathon 2026 and choose this category.

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