---
title: "Realtime voice AI - OpenAI WebRTC Implementation. Live app. Open source."
slug: realtime-voice-ai-openai-webrtc-implementation-live-app-open-source
date_published: 2025-01-19T13:49:37.888Z
original_url: https://www.tigzig.com/post/realtime-voice-ai-openai-webrtc-implementation-live-app-open-source
source: migrated
processed_at: 2025-12-02T10:00:00.000Z
---

# Realtime voice AI - OpenAI WebRTC Implementation. Live app. Open source.

![REX-RT Voice AI](https://static.wixstatic.com/media/ef0c19_62b1b47abcf94249a27e1ca383470d9d~mv2.png/v1/fill/w_350,h_221,al_c,q_85,usm_0.66_1.00_0.01,enc_avif,quality_auto/ef0c19_62b1b47abcf94249a27e1ca383470d9d~mv2.png)

The real-time AI space is moving fast. Exploring options for real-time, conversation-driven analytics and DB-backed interactions.

OpenAI's revamped Realtime API, Google's Flash 2.0 Experimental, and ElevenLabs' Conversational AI are all raising the bar while driving down costs.

Built **REX-RT**, a mobile-friendly app powered by OpenAI's Realtime API. It connects to a PostgreSQL database with 1.5M cricket records-live, searchable, and fast. Using WebRTC, gpt-40-mini, and function calling. **Costs ~$0.05–$0.07 per minute.** Fully customizable.

While the demo uses cricket data, it can support domains like finance, retail and beyond.

**Performance:** Solid, but not plug-and-play. Needs deep dives into event flows, WebRTC, and APIs. It's a code-and-build journey.

Last week, I shared **REX-C** ([cricket-odi.tigzig.com](http://cricket-odi.tigzig.com/)), built with Eleven Labs' Realtime Voice Widget. Plug-and-play simplicity, excellent performance (~$0.20/min), but limited UI customization-unless you use their SDK.

The possibilities: Huge. Conversation-driven analytics, voice integrations, realtime DB backed conversations, scalable apps for enterprises.

**Other tools to explore:** Gemini 2.0 Flash Experimental, Eleven Labs, Hume AI and packaged ones like Vapi, Bland AI, Synthflow and others.

---

## Tech Details-Source Code

REX-RT is built with **Cursor AI**. Vanilla JS/HTML with CSS for mobile responsiveness, FastAPI for DB connectivity, and Flowise AI for LLM agents. Basic security is via domain whitelisting.

Most of my apps lean on React, but loving Vanilla JS for its raw power and speed to deploy-though it trades some polish for simplicity.

Source code, deployment guides and learning resources: [tigzig.com](http://tigzig.com/) 

**Caveat:** This is a working prototype with a single agent (gpt-40-mini). Data is sourced from [cricsheet.org](http://cricsheet.org/)'s experimental CSV section and isn't independently validated.