Eman Fatima
AI EngineerbuildingFullStack,LLM &AIapplicationdevelopment.
New to Contra
Eman is ready for their next project!
Built a PhysioCare AI Voice Receptionist designed to handle physiotherapy-related conversations through a natural voice interface.
The assistant can interact with users, understand their requests, and support common reception workflows for a physiotherapy service. The project focused on combining voice AI, LLM-based conversation, and backend/API workflows into a practical healthcare application.
Key areas included:
AI voice assistant
Conversational AI
LLM integration
Physiotherapy-focused workflows
Tool/function calling
API integration
Backend workflow development
Voice conversation flow and testing.
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Built a PhysioCare MCP Server that connects an AI assistant with physiotherapy-focused tools through the Model Context Protocol (MCP).
The project demonstrates tool calling for physiotherapy use cases, allowing an AI system to interact with structured healthcare-related functions instead of relying only on static responses.
Key work included:
MCP server development
Tool/function calling
Physiotherapy-focused AI tools
Structured tool inputs and outputs
AI-to-tool communication
API-based workflow
Testing and debugging of MCP tools
This project was built as a practical AI Engineering project to explore how MCP can connect LLM-powered applications with real-world domain-specific tools.
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Physio MCP Server is a healthcare-focused Model Context Protocol (MCP) server built with Python and FastMCP.
This project demonstrates MCP client-server communication with physiotherapy-related tools, including patient progress tracking and pain improvement analysis.
The server provides tools to process patient pain scores, attendance data, and generate simple rehabilitation progress insights through an MCP client.
Tech Stack:
Python, MCP, FastMCP, MCP Client-Server Architecture
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AI Healthcare Assistant — AI Agent
Built an AI-powered healthcare assistant designed to help users with general health and physiotherapy-related guidance through an interactive AI agent interface.
Key Features
AI-powered conversational healthcare assistance
User-friendly chat interface
Health and physiotherapy-focused responses
Interactive AI agent workflow
Practical healthcare use-case implementation
Focus on accessible and personalized user support
Tech Focus
AI Engineering, LLMs, AI Agents, Python, API integration, and healthcare-focused AI workflows.
This project demonstrates my ability to design and build practical AI solutions for real-world healthcare use cases.
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