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A showcase of production-grade systems — from enterprise healthcare SaaS to full-stack web platforms and AI/ML integrations.
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🏥 Featured · Active Development
SmartSchedule Healthcare
Enterprise-grade AI-powered healthcare appointment scheduling SaaS platform built on .NET 9/10 and Azure. Targets a $150B market with projected 40% no-show reduction and 50% administrative efficiency gains for healthcare providers.
In Development — MVP Q2 2026
.NET 9C#ASP.NET CoreBlazor WebAssembly+20
TinyTots — E-Commerce Platform
Full-stack e-commerce platform for eco-friendly children's and women's clothing. Features ML-powered product recommendations, secure M-Pesa payment integration, real-time order tracking, and 85% test coverage.
React 18TypeScriptNode.jsExpress.js+6
Real-Time Collaboration Platform
Collaborative coding platform with real-time document editing, live presence detection, and WebSocket-based communication. Built with a scalable microservices backend and Redis session management.
TypeScriptReactNode.jsSocket.io+4
AI-Powered Customer Support Chatbot
Intelligent chatbot for e-commerce platforms achieving 95% accuracy in intent recognition and reducing customer support response time by 40% using NLP and TensorFlow.
PythonTensorFlowNLPREST APIs+2
BlazorCrudDemo — Clean Architecture Showcase
Production-ready .NET CRUD application demonstrating clean layered architecture, SOLID principles, real-time SignalR updates, and Entity Framework Core — built as a reference implementation for enterprise .NET patterns.
C#.NET CoreBlazor ServerEntity Framework Core+4
COVID-19 Data Visualization Dashboard
Interactive dashboard visualising COVID-19 statistics across East Africa with real-time data updates, D3.js charts, and responsive design for all devices.
JavaScriptD3.jsHTML5CSS3+1
Microservices Architecture with CI/CD
Containerised microservices application with automated testing, full deployment pipeline, and AWS cloud orchestration — demonstrating production DevOps practices.
DockerKubernetesGitHub ActionsAWS+1
Email Signature Extraction with LLMs
A research-oriented Python project that evaluates and compares the performance of large language models — OpenAI GPT-3.5 Turbo and Anthropic Claude 3 — on the task of extracting structured data from email signatures and outputting clean JSON.
PythonCCUDAC+++3