From zero
to deployed and paid.
Five phases. No shortcuts. Every phase builds on the last until you graduate with skills, a product, and traction.
The methodology behind
every product we ship.
Not a curriculum. A repeatable six-step system for building AI products that real users pay for — from first principle to scale.
Why it works: You cannot advance to the next step until the previous one is verified by real user behaviour. Most programs skip validation. This framework makes it structurally impossible.
Four cohorts in.
Here is what happened.
Problems worth solving.
Built by our engineers.
A sample of products built across our cohorts. Each one started with a real problem, a real user, and a fixed deadline to ship.
Crop disease detection
Identifies crop diseases from a phone photo. Built for smallholder farmers who have no agricultural extension officer nearby.
Clinic triage assistant
Helps under-resourced clinics prioritise patients before a doctor is available. Designed around the workflow of real nurses.
Alternative credit scoring
Uses M-Pesa transaction history to assess creditworthiness for people who have never held a formal bank account.
Cross-border freight router
Optimises routes and loads for trucks moving goods across East Africa. Built after talking to 40+ drivers and clearing agents.
Produce price forecaster
Tells farmers what prices to expect at different markets before they travel. Trained on historical price and weather data.
Technical skills verifier
Helps African tech companies assess candidates beyond their CV. Built because our own graduates kept losing to résumé screening.
Two tracks.
One standard of excellence.
AI Product School
Build and ship AI products — from engineering foundations to a product with paying users.
- Full 5-phase journey from foundations to launch
- LLM engineering, MLOps, system design
- Labs access — GPU compute, real datasets, weekly sprints
- Co-founder matching and incubation pipeline
Technical Writing
The engineers who can document AI become indispensable.
- API documentation, model cards, AI release notes
- Docs-as-code: Git, Markdown, CI/CD pipelines
- Developer experience strategy and content architecture
- Portfolio of real documentation work