DEV Community: Domonique Luchin

Building a Multi-Tenant AI Stack for 6 Businesses: Load Bearing Empire's VAPI + Asterisk + Supabase Architecture

Domonique Luchin — Thu, 12 Nov 2026 10:00:10 +0000

Building a vertically integrated business empire on bootstrapped capital means every infrastructure decision has to earn its keep. When Domonique Luchin consolidated Load Bearing Capital, Load Bearing Demo, Quiet Hours Valet, Luchin Credit Repair, Petroleum Noir, and StructCalc AI under a unified AI stack, the requirements were brutal: eliminate SaaS sprawl (Twilio, SendGrid, Make.com), route intelligent voice agents across six distinct business models simultaneously, and maintain 98-table database integrity across heterogeneous workflows—all on self-hosted infrastructure. This article documents how we deployed the LB Empire OS: a JARVIS-pattern Brain Router orchestrating 26 VAPI agents, Asterisk 18.10 PBX on Vultr, and Supabase (project whxtjboruayowkqyjvcq) with 42 Edge Functions handling everything from real estate lead qualification to valet task distribution, built to scale from a phone into a full operational infrastructure without the cloud bill.

Building a Multi-Agent Voice Command Center: QHV's 8-AI Architecture on Supabase + VAPI

Domonique Luchin — Tue, 10 Nov 2026 10:00:12 +0000

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We built Quiet Hours Valet's command center in 60 days: 8 concurrent AI voice agents orchestrated through VAPI, backed by a custom Postgres schema across 8 tables, with real-time webhook processing via Supabase Edge Functions. This isn't a chatbot—it's a production dispatch system handling valet operations, customer routing, and service automation at scale. Here's how we architected it: frontend deployed to Vercel, VAPI agents handling natural language intent routing, Asterisk PBX integration for legacy phone systems, and a Supabase database layer managing agent state, conversation logs, and automation triggers. If you're building voice-first infrastructure for service businesses or wrestling with how to wire VAPI webhooks into a reliable backend, this breakdown covers the schema design decisions, Edge Function patterns, and deployment strategy that let us go from zero to production without overhauling infrastructure.

Building a Multi-Agent Voice Command Center: QHV's Real-Time Waste Management Dashboard with VAPI, Supabase, and Vercel

Domonique Luchin — Sun, 08 Nov 2026 10:00:10 +0000

OPENING: Quiet Hours Valet processes thousands of trash collection requests across multiple properties, but coordinating pickups, confirmations, and customer service at scale required something beyond traditional call centers. We built a custom command center integrating eight AI voice agents powered by VAPI, a Supabase-backed Postgres schema with eight normalized tables, and Vercel-deployed frontend pages that handle real-time dispatch, customer interactions, and operational automation. This article walks through our technical architecture—including how we implemented Supabase Edge Functions to manage VAPI webhook callbacks, designed our relational schema for concurrent request handling, and deployed a stateful voice command system that reduced manual coordination overhead by 70%. If you're building voice-first operations platforms, scaling call center infrastructure, or integrating LLM agents into logistics workflows, this deep dive covers the exact patterns we used to turn Load Bearing Empire's trash valet service into an automated dispatch engine.

Building a $10M AI Stack on Supabase + VAPI: How Load Bearing Empire Eliminated SaaS Bloat

Domonique Luchin — Sat, 07 Nov 2026 10:00:09 +0000

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We ditched Twilio, SendGrid, and Make.com. Instead, we built Load Bearing Empire's entire operational backbone—6 businesses, 26 VAPI AI agents, and 98 Supabase tables—on self-hosted Asterisk 18.10 and a custom JARVIS Brain Router running on Vultr. This article breaks down the exact infrastructure decisions, database architecture, and agent routing logic that powers our real estate wholesaling, demolition, credit repair, and mineral rights operations from a single pane of glass. We'll cover Supabase's Edge Functions for real-time orchestration, why Asterisk PBX replaced traditional telecom, and the cost-per-transaction math that makes this stack sustainable for vertically integrated businesses.

Multi-County Real Estate Data Pipeline: Integrating CrawlOS with Supabase for Automated HCAD Scraping Across 14 Houston-Area Cou

Domonique Luchin — Fri, 06 Nov 2026 10:00:12 +0000

Building a scalable real estate wholesaling operation requires more than just capital—it requires reliable, automated data infrastructure. At Load Bearing Empire, we process county appraisal district data across 14 Houston-area counties using a custom CrawlOS integration layered on top of Supabase, PostgreSQL, and pg_cron jobs. This article walks through our production setup: how we modeled the crawlos schema with 17 normalized tables, deployed the lbc-hcad-actor to systematically extract property records from Harris County Appraisal District APIs, and configured nightly batch runs that surface ~125 scored leads per execution. If you're running real estate data operations at scale and need a self-hosted alternative to expensive data aggregation services, this technical breakdown covers the schema design, actor configuration, cron scheduling, and lead-scoring logic that powers our lead generation pipeline.

Building a Vertically Integrated AI Stack Across 6 Businesses: Load Bearing Empire's Infrastructure Architecture with Supabase,

Domonique Luchin — Wed, 04 Nov 2026 10:00:14 +0000

In March 2026, Load Bearing Empire deployed a unified AI infrastructure layer across six operationally distinct businesses—real estate wholesaling, demolition, valet services, credit repair, mineral rights, and structural SaaS—using a self-hosted stack built on Supabase, VAPI voice agents, Asterisk 18.10 PBX, and a custom JARVIS Brain Router. By eliminating third-party dependencies (Twilio, SendGrid, Make.com), the company achieved cost parity with legacy platforms while gaining full architectural control: a single Supabase project (whxtjboruayowkqyjvcq) managing 98 normalized tables and 42 Edge Functions, 26 concurrent VAPI agents handling inbound/outbound workflows, and Asterisk 18.10 running on Vultr infrastructure for call routing and IVR logic. This technical deep-dive documents the stack decisions, integration patterns, and operational lessons from consolidating disparate business processes into a single, AI-native infrastructure layer.

Building a 14-County Real Estate Data Pipeline with Supabase, CrawlOS, and pg_cron

Domonique Luchin — Tue, 03 Nov 2026 10:00:11 +0000

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Real estate wholesaling at scale demands real-time property data across fragmented county databases. This article walks through integrating CrawlOS into a Supabase backend to build a production 14-county scraper covering the Houston metropolitan area (HCAD tax records), with automated nightly runs via pg_cron generating ~125 qualified leads per execution. We'll cover the crawlos schema design (17 tables), actor deployment strategy for lbc-hcad-actor, Supabase foreign key relationships for lead scoring, and operational patterns that transform raw property records into actionable wholesale opportunities—all running on self-hosted infrastructure without third-party data brokers.

Building a Multi-Agent Voice Command Center: Load Bearing Empire's QHV Architecture on Supabase + VAPI

Domonique Luchin — Mon, 02 Nov 2026 10:00:11 +0000

At Load Bearing Empire, we just shipped a production voice command center handling 8 concurrent AI agents across real estate, demolition, and logistics operations—all orchestrated through Supabase Edge Functions and VAPI webhooks. This article breaks down the technical stack we deployed to Vercel: a 5-page frontend managing 8 specialized voice agents, 8 distinct automation workflows, and an 8-table Postgres schema that routes customer interactions to the right agent (property valuation, demo scheduling, valet trash requests, credit repair intake, mineral rights deals, structural calculations, capital sourcing, and emergency dispatch). If you're building multi-agent systems without a dedicated backend team, using Supabase for webhook handling and Postgres as your agent state machine is a practical, cost-effective approach—here's exactly how we wired it.

Building a Multi-Tenant AI Stack on Supabase + VAPI: How Load Bearing Empire Unified 6 Businesses with 26 Agents and Zero Third-

Domonique Luchin — Sun, 01 Nov 2026 10:00:12 +0000

Load Bearing Empire's infrastructure consolidation eliminated $8K+ in monthly SaaS spend by replacing Twilio, SendGrid, and Make.com with a self-hosted stack: Supabase (98 tables, 42 Edge Functions), 26 VAPI voice agents routed through a custom JARVIS Brain Router, and Asterisk 18.10 PBX on Vultr. This article documents the technical architecture powering real estate wholesaling, demolition, valet services, credit repair, mineral rights, and structural SaaS from a single PostgreSQL project—including agent routing logic, webhook handling patterns, and the decision to self-host instead of vendor-lock.

Building a Multi-Agent Voice Command Center: Supabase Edge Functions, VAPI, and Real-Time Automation at Scale

Domonique Luchin — Fri, 30 Oct 2026 10:00:12 +0000

We built Quiet Hours Valet's command center from scratch—5 frontend pages orchestrating 8 concurrent AI voice agents, 8 automation workflows, and an 8-table Postgres schema—all running on Supabase Edge Functions and VAPI webhooks. This article breaks down the architecture decisions, the webhook-to-automation pipeline that powers our valet trash operation, and how we use Supabase's serverless compute layer to route customer calls into parallel agent workflows without managing a single server. If you're building voice-first SaaS, integrating real-time AI agents with a relational database, or trying to scale Asterisk PBX infrastructure without losing your mind, this technical deep-dive covers the routing logic, schema design, and deployment strategy we used to move from local development to production on Vercel.

Building a Multi-County Real Estate Data Pipeline with CrawlOS, Supabase, and pg_cron

Domonique Luchin — Thu, 29 Oct 2026 10:00:10 +0000

OPENING PARAGRAPH:

Real estate wholesaling operates on volume and speed—you need qualified leads faster than your competition can find them. At Load Bearing Capital, we built a horizontally scalable scraping infrastructure that covers all 14 Houston-area counties using CrawlOS, integrated directly into Supabase with pg_cron orchestration. This article details our technical stack: a 17-table CrawlOS schema handling county-specific HCAD (Harris County Appraisal District) data ingestion, automated nightly actor runs via our lbc-hcad-actor, and a qualification pipeline that consistently surfaces ~125 scored leads per execution. If you're building property data infrastructure on Supabase and need to move beyond single-county scrapers, here's exactly how we architected ours.

Building a Multi-Agent Voice Automation Command Center: Load Bearing Empire's QHV Stack (Supabase + VAPI + Vercel)

Domonique Luchin — Wed, 28 Oct 2026 10:00:11 +0000

OPENING: We built Quiet Hours Valet's command center from scratch in 2026—a full-stack voice automation platform handling 8 concurrent AI agents, 8 automation workflows, and real-time dispatch across a trash valet operation. Here's the technical breakdown: Supabase Edge Functions handling VAPI webhook ingestion, a normalized 8-table Postgres schema for agent state management, 5 responsive frontend pages deployed on Vercel, and self-hosted Asterisk PBX integration for phone routing. If you're building polyglot AI systems that need to scale beyond chatbots into actual business operations, this stack handles order fulfillment, customer routing, callback queuing, and agent orchestration without the SaaS tax. We'll walk through the architecture decisions, the Postgres schema that made webhook processing sane, and why Edge Functions beat traditional serverless for latency-critical voice automation.