DEV Community: Domonique Luchin

Building a Multi-Agent Voice Command Center: Load Bearing Empire's QHV Architecture with VAPI, Supabase Edge Functions, and Verc

Domonique Luchin — Wed, 21 Oct 2026 10:00:10 +0000

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We built Quiet Hours Valet's command center in 72 hours—a production voice operations platform handling 8 concurrent AI agents, 8 automation workflows, and real-time dispatch across a fleet of valet services. This article breaks down the technical stack we chose: VAPI for voice inference, Supabase Edge Functions for webhook processing at scale, a normalized 8-table Postgres schema for agent state management, Asterisk PBX for legacy integration, and Vercel for deployment. If you're scaling voice AI beyond proof-of-concept—multi-tenant SaaS, field operations, or compliance-heavy workflows—this is a battle-tested architecture that handles concurrent agent invocations, webhook reliability, and stateful automation without serverless timeouts or cold starts.

Building a Multi-Agent Voice Command Center: How We Scaled 8 AI Agents on Supabase Edge Functions and VAPI

Domonique Luchin — Mon, 19 Oct 2026 10:00:12 +0000

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We just shipped the Quiet Hours Valet Command Center—a production voice automation platform handling 8 concurrent AI agents, 8 automation workflows, and real-time webhook processing through Supabase Edge Functions. This post breaks down the architecture we built: a 5-page frontend on Vercel, an 8-table Postgres schema for agent state management, Asterisk PBX integration with VAPI for voice orchestration, and the Edge Function patterns that let us route, scale, and persist multi-agent conversations without spinning up dedicated servers. If you're building voice AI products on a vertical integration stack, this is how we did it.

Building a Multi-Agent Voice Command Center: Load Bearing Empire's QHV Stack on Supabase, VAPI, and Vercel

Domonique Luchin — Sat, 17 Oct 2026 10:00:11 +0000

We built a production voice command center handling 8 concurrent AI agents, 8 automation workflows, and real-time state management across 5 frontend interfaces—all on Supabase Edge Functions, VAPI's voice API, and Vercel deployments. This article breaks down the architecture: how we designed an 8-table Postgres schema to coordinate valet trash operations, real estate wholesaling, and credit repair workflows through a single webhook listener; how we structured VAPI agent callbacks to trigger state mutations without race conditions; and the specific infrastructure decisions that let us ship this from a self-hosted Asterisk PBX backbone. If you're running a service business (field ops, logistics, customer contact) and considering voice automation, this is the technical blueprint we used to move from proof-of-concept to production at Load Bearing Empire.

Building a Vertically Integrated AI Stack: How Load Bearing Empire Eliminated SaaS Dependencies with Supabase, VAPI, and Self-Ho

Domonique Luchin — Fri, 16 Oct 2026 10:00:09 +0000

Load Bearing Empire deployed a unified AI infrastructure across six distinct business units—real estate wholesaling, demolition, valet services, credit repair, mineral rights, and structural SaaS—by replacing proprietary platforms with an open-source backbone. The architecture centers on Supabase (98 tables, 42 Edge Functions), VAPI (26 autonomous agents), Asterisk 18.10 PBX, and a custom JARVIS Brain Router orchestrating inbound workflows across all divisions. By eliminating Twilio, SendGrid, and Make.com integrations, the stack reduced vendor lock-in while enabling real-time data synchronization—a critical requirement for businesses operating at the intersection of hardware (demolition, valet logistics) and digital services (credit repair, SaaS). This article walks through the infrastructure decisions, deployment strategy on Vultr, and the trade-offs between self-hosting and managed services when scaling AI agents across operationally distinct revenue streams.

Building a 14-County Real Estate Data Pipeline with CrawlOS and Supabase: Automated Lead Scoring at Scale

Domonique Luchin — Thu, 15 Oct 2026 10:00:09 +0000

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Real estate wholesaling at scale demands more than spreadsheets—it demands infrastructure. This article walks through integrating CrawlOS into a production Supabase environment to automate property data collection across 14 Houston-area counties, using pg_cron for nightly orchestration and HCAD (Harris County Appraisal District) scraping to generate ~125 qualified leads per run. We'll cover the 17-table CrawlOS schema design, actor deployment patterns, lead scoring logic, and the gotchas we hit moving from prototype to production on self-hosted infrastructure. If you're building a vertical real estate tech stack or scaling data pipelines beyond a single county, this technical deep-dive shows exactly how to structure crawlers, database relationships, and automation for wholesale deal flow.

Building a Multi-Agent Voice Command Center: Load Bearing Empire's QHV Architecture with VAPI, Supabase Edge Functions, and Aste

Domonique Luchin — Wed, 14 Oct 2026 10:00:10 +0000

OPENING:

We built a production voice command center handling 8 concurrent AI agents across valet, real estate, and demolition operations—all coordinated through a single Supabase backend. This article breaks down the technical architecture: how we structured VAPI webhook integrations in Supabase Edge Functions, designed an 8-table Postgres schema to track agent states and automation scenarios, and deployed a 5-page frontend on Vercel that manages everything from voice routing to task automation. If you're scaling voice AI beyond chatbots into operational infrastructure, this is the stack breakdown you need.

Building a Vertically Integrated AI Stack: How Load Bearing Empire Consolidated 6 Businesses on Supabase, VAPI, and Self-Hosted

Domonique Luchin — Tue, 13 Oct 2026 10:00:10 +0000

When you're running real estate wholesaling, demolition, valet services, credit repair, mineral rights, and SaaS simultaneously, vendor lock-in isn't an option—it's a liability. This is the infrastructure story behind Load Bearing Empire: a complete rip-and-replace of Twilio, SendGrid, and Make.com automation in favor of a self-hosted backbone built on Supabase, VAPI's multi-agent orchestration, Asterisk 18.10 PBX on Vultr, and a custom JARVIS Brain Router handling cross-business logic. We deployed 26 specialized VAPI agents across operational workflows, connected them to a single 98-table Supabase instance (project ID: whxtjboruayowkqyjvcq) running 42 Edge Functions, and eliminated $8K+ monthly in SaaS overhead while cutting integration latency to sub-100ms. Here's what it actually cost to build, why the traditional startup stack failed at scale, and the exact architecture decisions that let one engineer operate six revenue streams from Day One.

Building a Multi-Agent Valet Trash Command Center: Supabase Edge Functions, VAPI, and Postgres at Scale

Domonique Luchin — Sun, 11 Oct 2026 10:00:10 +0000

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We built a production command center for Quiet Hours Valet handling 8 concurrent AI voice agents, 5 frontend pages, and 8 discrete automation workflows—all orchestrated through Supabase Edge Functions triggering VAPI webhooks and a normalized 8-table Postgres schema. This technical deep-dive covers the architecture decisions behind a vertically integrated waste services operation running on self-hosted infrastructure, how we structured real-time voice agent coordination through serverless functions, database indexing strategies for high-frequency webhook ingestion, and the deployment pipeline we use to ship updates to Vercel without disrupting live voice calls. If you're building multi-agent systems with Asterisk PBX, VAPI, or need Postgres + Supabase for voice automation at scale, this is the reference architecture.

Building a Unified Command Center: 8 AI Voice Agents, Real-Time Automation, and Supabase Edge Functions in Production

Domonique Luchin — Thu, 08 Oct 2026 10:00:10 +0000

OPENING: We built a vertically integrated command center for Load Bearing Empire's valet trash operation—5 frontend pages orchestrating 8 concurrent VAPI voice agents, 8 discrete automation workflows, and a Postgres schema handling real-time dispatch, customer management, and billing across six tables. The backbone: Supabase Edge Functions consuming VAPI webhooks, Asterisk PBX for telephony routing, and a self-hosted infrastructure stack deployed to Vercel. In this post, we'll break down the architecture, the webhook pattern that keeps agents and automations in sync, and how we scaled from a single-person operation to a system handling hundreds of concurrent interactions—without leaving the tech debt of traditional call center software.

Building a Multi-Agent Voice Command Center: How We Deployed 8 AI Agents on Supabase Edge Functions and Vercel

Domonique Luchin — Tue, 06 Oct 2026 10:00:08 +0000

OPENING: We just shipped a production voice command center handling 8 concurrent AI agents orchestrated through VAPI webhooks, Supabase Edge Functions, and a normalized 8-table Postgres schema. This article breaks down the technical architecture we used to route real-time voice interactions across Load Bearing Empire's operations—from valet trash automation to real estate workflows—without managing a single dedicated server. We'll cover webhook validation patterns, agent state management in Postgres, Vercel serverless scaling, and the exact automation scenarios that let us handle high-concurrency voice traffic at the edge.

Building a Multi-Vertical AI Stack on Supabase and Asterisk: How Load Bearing Empire Replaced Twilio + SendGrid with Self-Hosted

Domonique Luchin — Mon, 05 Oct 2026 10:00:08 +0000

From a single phone, Domonique Luchin architected a vertically integrated AI empire spanning real estate wholesaling, demolition, credit repair, and mineral rights—all powered by a unified tech stack. This deep dive covers the infrastructure decisions behind Load Bearing Empire's migration away from vendor lock-in (Twilio, SendGrid, Make.com) toward a self-hosted backbone: Supabase (98 tables, 42 Edge Functions) as the source of truth, VAPI orchestrating 26 AI agents, Asterisk 18.10 running enterprise-grade telephony on Vultr, and a custom JARVIS Brain Router tying it all together. We'll walk through the technical architecture that lets a single engineer manage six revenue-generating businesses in parallel—schema design, stateless agent routing, PBX configuration for multi-tenant voice, and the cost/reliability trade-offs that made self-hosting worth the operational overhead.

Building a Vertically Integrated AI Stack on Supabase and Asterisk: How Load Bearing Empire Unified 6 Businesses

Domonique Luchin — Sat, 03 Oct 2026 10:00:09 +0000

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When you're running multiple service lines—real estate wholesaling, demolition, valet, credit repair, mineral rights, and SaaS—across different tech stacks, you hemorrhage money on integrations. We eliminated Twilio, SendGrid, and Make.com entirely, replacing them with a single Supabase instance (project ID: whxtjboruayowkqyjvcq), 26 VAPI agents, and Asterisk 18.10 PBX hosted on Vultr. This article breaks down the technical architecture we deployed on March 17, 2026: how we built a JARVIS Brain Router to orchestrate inbound calls across businesses, structured 98 relational tables for cross-domain data (real estate transactions, demolition scheduling, valet routing, credit scores, mineral lease tracking, and SaaS usage), connected 42 Edge Functions for real-time business logic, and eliminated vendor lock-in at every layer. If you're running a portfolio of AI-first businesses and want to ship faster while cutting AWS bills by 60%, this is the stack that made it possible.