McTaba's Software & AI Engineering curriculum spans 30 weeks across five phases: web fundamentals and M-Pesa integration (Weeks 1 to 6), data and CRM engineering with WhatsApp and USSD (Weeks 7 to 13), commerce and fintech with multi-payment systems (Weeks 14 to 22), production engineering with Docker and microservices (Weeks 23 to 26), and a capstone sprint building a multi-tenant business platform (Weeks 27 to 30). AI engineering skills run throughout from Phase 2 onward.
You start where every serious developer should: understanding how the web actually works before touching a framework. This phase covers HTML5 semantic markup, CSS3 layout and responsive design, JavaScript ES6+ with async/await, the HTTP protocol, REST API design, and Git workflows including branching, pull requests, and code review etiquette. By the end of Week 6, you deploy your first production project: an M-Pesa Paylink and Receipt Mini-App that triggers STK Push payments and generates PDF receipts. This is not a toy project. It processes real mobile money transactions using the Safaricom Daraja API.
Phase 2 adds databases, authentication, and your first African Stack integrations beyond payments. You learn MongoDB with Mongoose for document storage, PostgreSQL for relational data, and JWT-based authentication with proper security practices. The big additions here are the WhatsApp Business API and USSD engineering via Africa's Talking. You build two projects: a WhatsApp Lead Capture CRM that turns conversations into structured leads with a web dashboard, and a USSD Customer Self-Service App where users dial a short code to check balances and request support. This is also where AI engineering enters the curriculum. You start learning how LLMs work, build your first basic agent, and begin using AI tools in your development workflow.
The longest and most intensive phase. You move into Next.js for server-side rendering, learn state machine patterns for managing complex order flows, and integrate multiple payment providers: M-Pesa, Airtel Money, and Stripe. Webhook security and idempotency become critical skills here because you are handling real money flows. You also learn Telegram Bot API and cron-based automation for scheduled tasks. Two projects: an E-commerce platform with cart, checkout, payments, and automatic WhatsApp order notifications, and a Chama Savings Platform with recurring M-Pesa collections, group balance tracking, and automated contribution reminders via Telegram. On the AI side, you work with RAG (Retrieval-Augmented Generation) to build systems that give LLMs access to business-specific data, and you practice context engineering to control LLM behavior reliably.
Phase 4 focuses on the skills that separate a developer who can build features from one who can ship and maintain production systems. You learn Redis and BullMQ for job queues, microservices architecture patterns, Docker for containerization, CI/CD pipelines with GitHub Actions, failover logic, and SMS gateway integration. Your projects: a Booking and Appointments System with M-Pesa deposit handling and SMS reminders, and a Multi-Channel Notification Hub that provides a unified API to route alerts through WhatsApp, SMS, or email with automatic failover when one channel goes down. By this point, AI engineering is integrated into your workflow naturally. You are building AI-powered features into your projects, not treating AI as a separate skill.
Everything comes together. You build The African SME OS: a multi-tenant operating system for small businesses that combines payments (M-Pesa, Airtel, Stripe), USSD self-service, WhatsApp automation, and CRM into one platform. This project requires multi-tenant architecture, role-based access control, full audit logging, advanced API design, and deployment at scale. It is the most complex thing you will have built, and it demonstrates every skill from the previous four phases working together. You also have the option to build a custom startup project if you have a specific product idea. The phase ends with Demo Day, where you present your capstone to peers, mentors, and invited employers.
AI engineering is not a standalone module at the end. It is introduced in Phase 2 and woven through every subsequent phase. Here is how it progresses:
The goal is not to make you an ML researcher. It is to make you a developer who can build and ship AI-powered products for real users. That means agents, tool-use patterns, RAG, and context engineering, not neural network theory.
Three things separate this from a standard bootcamp curriculum:
The African Stack is the default, not an afterthought. Most bootcamps teach Stripe for payments and stop there. This curriculum teaches M-Pesa (Daraja API), Airtel Money, WhatsApp Business API, USSD via Africa's Talking, and Telegram bots because those are the systems that businesses in Kenya, Nigeria, Uganda, and across the continent actually use. If you plan to work in or build for African markets, this matters.
AI engineering is built in from the start. You do not finish 20 weeks of web development and then get a 2-week "intro to AI" tacked on. AI skills are introduced in Phase 2 and practiced through every project afterward. By graduation, using AI tools and building AI features is part of how you work, not something you learned in theory.
Every project ships. You deploy 8 applications during the program. Not localhost demos. Not GitHub repos that nobody visits. Deployed, working applications with real API integrations that you can show in a job interview or to a freelance client. Your portfolio is your credential.
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The program runs five days a week with a mix of instruction and hands-on building:
Expect to spend 20 to 25 hours per week between classes and independent project work. Some weeks in Phase 3 (the commerce and fintech phase) will demand more because the payment integrations require careful testing.
Here is every major technology covered in the curriculum:
Frontend: HTML5, CSS3, JavaScript ES6+, React, Next.js
Backend: Node.js, Express, REST APIs, server-side rendering
Databases: MongoDB (Mongoose), PostgreSQL, Redis
Payments: M-Pesa Daraja API (STK Push, C2B, B2C), Airtel Money, Stripe, Paystack
Communication: WhatsApp Business API, USSD (Africa's Talking), Telegram Bot API, SMS gateways, email (SendGrid)
AI Engineering: LLM fundamentals, AI agents, RAG, context engineering, AI coding tools (Copilot, Claude, Cursor)
DevOps: Git, GitHub Actions (CI/CD), Docker, BullMQ job queues, cron scheduling
Architecture: Microservices, multi-tenant design, state machines, webhook patterns, failover logic
You will not master every item on this list in 30 weeks. That is not the point. You will build working systems with each of them, understand when and why to use them, and have the foundation to go deeper on your own after the program.
KES 120,000
The complete Full-Stack Software & AI Engineering course. From zero code to AI-powered products in 16 weeks. 16 modules, 208 lessons, with career support until placement.
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