Building a Multi-Agent Voice Command Center: How Load Bearing Empire Scaled 8 AI Agents on Supabase Edge Functions and VAPI
OPENING PARAGRAPH: We just shipped the QHV Command Center—a production voice automation platform handling 8 concurrent AI agents across valet, credit repair, and real estate operations. This article breaks down the technical architecture: how we architected an 8-table Postgres schema on Supabase, built stateful webhook handlers with Edge Functions to integrate VAPI's voice AI, designed 5 frontend…
We recently launched the QHV Command Center, a production voice automation system that can handle eight concurrent AI agents simultaneously. These agents are used in various operations such as valet services, credit repair, and real estate. This article will delve into the technical details of the system's architecture.
The eight-table Postgres schema was designed and implemented on Supabase. Stateful webhook handlers were developed using Supabase Edge Functions to integrate VAPI's voice AI technology. Additionally, five frontend interfaces were created in React, and eight distinct automation scenarios were orchestrated without adding any extra infrastructure overhead.
This stack, comprising Supabase, VAPI, and Vercel, significantly reduces deployment friction while maintaining a latency of under 200 milliseconds for webhook responses. The article will provide a step-by-step walkthrough of the schema design, webhook state management, and methods for handling concurrent agent routing. It's important to note that a single call can often trigger multiple downstream automations.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
