The project involves developing a mobile application and a web-based admin panel for a platform that allows users to rent personal items. The system supports three key user roles. General users can search, view, and book rental items, make payments, and provide feedback. Item owners can post items for rent, manage their listings and availability, and receive payments. Admin users manage the overall platform including users, item listings, payments, content, and advertisements via the backend panel.
The platform is built using the MEAN stack for the backend and React Native for mobile apps. It integrates with Google Maps, Facebook, Twitter, and payment gateways, and includes SSL encryption for secure transactions. The application supports both Arabic and English languages and offers features such as live chat, push notifications, and social media integration.

Medical Billing is a cloud-based charge capture solution designed for doctors to streamline patient list, accelerate billing, and optimise revenue capture by retrieving patient encounter details from EHR systems and leveraging Azure services. The solution aims to revolutionise healthcare charge capture by providing a user-friendly, efficient, and accurate solution that enhance revenue capture, improve operational efficiency and reduce administrative burdens for healthcare providers and organisations. By automating rounding list, the solution aims to facilitating streamlined patient assessment and service documentation during rounds, thereby enhancing overall workflow efficiency and billing accuracy. 
Built on Microsoft Azure, system leverages Azure App Services for a scalable web application and Azure Kubernetes Service (AKS) for efficient micro services deployment. 
Azure SQL Database and Azure Blob Storage securely manage patient records, financial transactions and medical documents, while Azure Key Vault ensures encryption and compliance with security standards. 
Azure API Management (APIM) enables seamless integration with third-party services.  
To automate claim processing and real-time tracking we have leveraged Azure Logic Apps and Azure Functions streamline data synchronisation and workflow execution.   

This project delivers an integrated AI and analytics platform for the Foundation for Sickle Cell Disease Research (FSCDR). It connects ECW (eClinicalWorks) EHR data with advanced AI-based predictive models to identify high-risk patients, automate follow-ups, and streamline state grant reporting (CMO99, CMOAE Tasks 5–13).
The goal is to transform static EHR data into actionable insights — improving research visibility, compliance, and patient outcomes while cutting down manual reporting workloads. The solution supports predictive ER-risk modeling, AI-based outreach automation, and Power BI-driven compliance dashboards across FSCDR’s Centers of Excellence.