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Rahees Ahmed

• Mar 14

A highly configurable, real-time voice AI assistant designed for medical practices to handle inbound patient calls, book appointments, request prescription refills, and intelligently route urgent queries. Built with FastAPI, Pipecat, LangGraph, Twilio, Deepgram, Cartesia, and Anthropic.

Architecture

Caller dials Twilio number → POST /inbound (Twilio webhook) → TwiML returns WebSocket URL → Twilio streams audio to /ws/{call_sid} → Pipecat pipeline starts → Deepgram STT (audio → text, ~200ms) → LangGraph Agent (text → intent + tool calls) → Cartesia TTS (text → audio, ~80ms) → Back to caller

try it on github: https://github.com/RaheesAhmed/MediVoice-AI

aiagent voiceagent healthcare

github.com

GitHub - RaheesAhmed/MediVoice-AI: A highly configurable, real-time voice AI assistant designed for medical practices to handle inbound patient calls, book appointments, request prescription refills, and intelligently route urgent queries. Built with FastAPI, Pipecat, LangGraph, Twilio, Deepgram, Cartesia, and Anthropic.

Azad Kumar

• 1d

I built an AI calling agent that can handle real-time conversations and automate tasks like appointment booking. It collects user details, manages conversations, and responds naturally to customers. Built using Spring Boot (Java) for backend logic, MongoDB for data storage, Twilio for call handling, and ElevenLabs for AI voice integration. ngrok was used for exposing local APIs during development and testing.

Due to client privacy and project confidentiality, the live demo and access link are not publicly available.

AI Agent Engineer Backend Development Prompt Engineering ElevenLabs MongoDB Spring Boot

Saransh Gautam

• 2d

A B2B SaaS platform for AI-powered image and video generation via business prompts. Credit-based billing system, real-time Firestore usage tracking, and tiered feature access. Built as Tech Lead from zero to production.