Project summary

My name is Victor. I trained as a veterinarian with an out-of-this-world love for animals, then my career took me somewhere else entirely: public health, development, philanthropy. It was good work. It just never felt like the work. I'm 28, and I kept coming back to the fact that the most fulfilled I've ever felt professionally was standing in a clinic or a field with an animal in front of me. So five months ago, I quit a senior role and started asking a simple question: how do I actually get back to serving animals, for real this time?

In that search, I found this. Nearly 5 billion farmed animals live in Africa right now, more than almost anywhere else on earth, and that number is growing faster here than anywhere else too. Only 1 to 2% of global farmed animal advocacy funding reaches the continent. Meanwhile, AI tools that monitor animal welfare, detecting distress and disease with 98% accuracy, already exist and work. They just don't exist here.

I am spending 16 weeks finding out what it would take to change that: which welfare indicators can actually be tracked with AI on the ground in East Africa, and which interventions are worth building first. The result is a scoping report and fundable next steps.

Africa's farms are industrializing fast and the welfare infrastructure isn't keeping up. A lot of people here don't think about animal welfare much at all, and honestly, I understand why: there are so many other urgent things competing for attention. But 5 billion animals is not a small number, and someone needs to start paying attention before the gap closes for good.

What are this project's goals? How will you achieve them?

The goal is to answer one question: Which AI-enabled welfare monitoring interventions are actually feasible to deploy in Africa's fastest-growing farmed animal sectors, and which one is worth funding first?

I'll get there in four phases over 16 weeks.

Phase 1: Landscape mapping. Map the scale and shape of East Africa's intensive animal agriculture across three species groups: poultry, farmed fish, and farmed insects, chosen because they represent the highest-volume, fastest-growing, and most neglected parts of the region's farming systems.

Phase 2: Welfare indicator assessment. For each species group, assess which animal-based welfare indicators, lameness, distress behavior, disease signs, mortality patterns, can realistically be monitored using AI and sensor technology that already exists or is close to market.

Phase 3: Ground-level consultation. Visit poultry, aquaculture, and insect farming operations directly, and talk to producers, welfare tech companies, and advocacy groups already working in the region.

Phase 4: Ranking and funding pathway. Shortlist 3 to 5 candidate interventions, rank them on feasibility and cost-effectiveness, and turn the strongest one into a concrete funding proposal ready to send to funders working in this space.

How will this funding be used?

Total request: $17,000

Researcher stipend: $9,600 ($2,400/month × 4 months). Covers full-time focus for 16 weeks: landscape research, welfare indicator analysis, field visits, stakeholder engagement, and writing. It's a living stipend and lets me treat this as my primary commitment for four months rather than something squeezed around other paid work.

Field visits and stakeholder engagement: $3,200. Direct travel to poultry, aquaculture, and insect farming operations across Kenya, local transport between sites, and modest costs tied to meeting producers, technology providers, and advocacy practitioners on the ground.

AI and research tooling: $1,200. Access to AI models and platforms for literature synthesis, data analysis, and drafting support across the research process, plus any specialized software needed to evaluate the technical feasibility of AI-based monitoring systems (computer vision, sensor data processing) for the species groups covered.

Data and materials: $1,000. Industry reports, market data, and any datasets not otherwise publicly accessible that are needed to ground the scale and feasibility estimates in the final report.

Contingency: $2,000 (roughly 9%). Agricultural data across much of East Africa is informal and unevenly documented, so this covers the extra time and cost that gap realistically requires, plus any unforeseen costs in accessing sites or stakeholders.

Who is on your team? What's your track record on similar projects?

Just me and 2 research assistants. I trained as a Doctor of Veterinary Medicine, which a critical foundation I intend to use here: I can assess welfare indicators like lameness, distress behavior, and disease signs directly.

I've also built and let go of a social enterprise before, (AnduBer) once it had grown solid enough legs to stand on its own, so I know what it takes to take an idea from nothing to something real and functioning. I have also spent my professional career in international development and philanthropy, running multi-country partner portfolios and advising on major institutional programmes, work that trained me to synthesize messy evidence into clear, actionable findings.

Five months ago I left a senior role to return to this. I don't have a research track record in animal welfare yet, because I'm early in this by design. What I bring instead is the rare combination of clinical training and a proven habit of finishing what I start.

What are the most likely causes and outcomes if this project fails?

Data scarcity is the main risk. Farmed animal data across East Africa is often informal and unevenly recorded in places. If a specific intervention doesn't seem feasible, the finding itself is still useful and it tells the field what needs to change before that path becomes fundable, and I still walk away with the relationships and ground knowledge this work will need regardless.

How much money have you raised in the last 12 months, and from where?

None yet. This would be my first funding.