Load Bearing Empire Builds Its Own AI Stack with Supabase and VAPI

Unifying six completely different business lines—real estate wholesaling, demolition, valet parking, credit repair, mineral rights, and a SaaS product—onto a single self-built AI infrastructure sounds like a crazy idea. But Load Bearing Empire did exactly that, and they didn't rely on a single SaaS vendor.

Why Vertical Integration Instead of SaaS Assembly

Load Bearing Empire's six business lines span both hardware operations (demolition requires scheduling heavy equipment, valet parking involves real-time logistics) and digital services (credit repair, SaaS product). This hybrid shape makes standardized SaaS solutions fall short.

The traditional approach is to select tools per business line: Twilio for communications, SendGrid for email, Make.com for automation orchestration. The problem is that data synchronization between these tools is asynchronous, with latency ranging from seconds to minutes. For scenarios that require real-time dispatch of vehicles at a demolition site or instant response to credit repair customer inquiries, this latency is unacceptable.

They chose a heavier path: using Supabase as the unified data layer (98 tables, 42 Edge Functions), VAPI to drive 26 autonomous AI agents, Asterisk 18.10 PBX for the phone system, plus a self-built routing layer called JARVIS Brain Router to orchestrate inbound workflows across all business lines.

The core logic behind this decision: when your business spans both the physical and digital worlds, real-time data synchronization matters more than the operational costs you save.

Breaking Down the Stack: What Each Component Solves

Supabase: More Than Just a Database

The scale of 98 tables and 42 Edge Functions shows that Supabase plays a role far beyond "managed Postgres" in this architecture. Edge Functions handle real-time events for each business line—such as equipment status changes in demolition or vehicle location updates in valet parking—and write these events directly into the corresponding data tables.

This design lets all business lines share the same data infrastructure while isolating their business logic through table structures. Compared to deploying independent database instances for each business line, operational complexity is dramatically reduced.

Supabase's Edge Functions are serverless functions based on the Deno runtime, deployed on edge nodes close to users. Compared to traditional centralized cloud functions, edge execution can significantly reduce network latency, which is especially important for event-driven scenarios requiring real-time responses (such as vehicle dispatch at a demolition site or status updates for valet parking). In Load Bearing Empire's architecture, Edge Functions act as a "business logic middle layer": they receive events from VAPI agents, the Asterisk phone system, or frontend applications, execute business rules, and then write to Postgres tables or trigger other Edge Functions. The scale of 42 functions indicates that each business line's real-time event processing logic is split into multiple fine-grained functions for independent deployment and debugging.

VAPI and 26 AI Agents

26 autonomous agents cover customer interaction scenarios across six business lines. Real estate wholesaling needs to screen potential sellers, credit repair needs to guide customers through submitting dispute materials, valet parking needs to confirm service times and locations—the conversational logic varies greatly across these scenarios, but they all run on VAPI's voice AI framework underneath.

The benefit of a unified framework is that knowledge can be reused across agents. For example, the logic for verifying customer identity in credit repair scenarios can be adapted for use in the mineral rights business.

VAPI (Voice AI Platform Interface) is a type of AI agent framework designed specifically for voice interaction scenarios. Its core difference from traditional IVR (Interactive Voice Response) systems is that traditional IVR relies on preset key menus and fixed flow trees, while frameworks like VAPI drive conversations based on large language models, capable of understanding natural language, dynamically adjusting conversation paths, and calling external APIs in real time during conversations to retrieve data or trigger business actions. In Load Bearing Empire's architecture, each AI agent is essentially a package of prompts, tool-call definitions, and business rule logic, deployed on the VAPI runtime.

Asterisk and JARVIS Brain Router

Asterisk 18.10 serves as the open-source PBX handling low-level phone routing, while JARVIS Brain Router determines which AI agent should take over each call, what data needs to be retrieved, and what workflows to trigger downstream. This routing layer is the scheduling hub of the entire architecture.

Asterisk is an open-source software PBX (Private Branch Exchange) that replaces the hardware switches in traditional enterprise phone systems with software, supporting SIP protocol, call routing, voicemail, conference bridging, and more. 18.10 is its LTS (Long Term Support) version. In AI agent scenarios, Asterisk typically plays the "last mile" role: it handles answering or making calls, establishes media stream channels, and then hands the audio stream to AI agents for processing. JARVIS Brain Router adds a decision layer on top of this chain—it's not simply call forwarding rules, but rather dynamically determines which AI agent to activate, which customer data to preload, and what downstream workflows to trigger after the conversation ends, based on information like caller number, time of day, and business tags. This "PBX + intelligent routing layer" division of labor keeps the underlying phone infrastructure stable, while business logic changes only require adjustments at the routing layer.

The Trade-off Between Self-Hosting and Managed Services

Deploying on Vultr means Load Bearing Empire has to handle server operations, security patches, and scaling on its own. In exchange, they gain complete control over data flows and deep integration capabilities with self-hosted components like Asterisk.

This choice has clear costs. The team needs DevOps capabilities to maintain Vultr instances, and the self-hosted version of Supabase also requires regular updates. For businesses without a technical team, this path isn't viable.

But for scenarios where six business lines operate simultaneously and require real-time data exchange between them, the integration flexibility that self-hosting brings outweighs the operational burden.

Implications for AI Infrastructure Selection

The Load Bearing Empire case points to a trend: as AI agents move from single scenarios to multi-business-line deployment, the depth of infrastructure integration matters more than the sophistication of individual point tools.

Tools like Twilio and SendGrid are each excellent, but their design assumption is "you are one of many customers." When your business needs real-time cross-system coordination, that assumption becomes a bottleneck.

The barrier to building your own stack is lowering—Supabase, VAPI, and Asterisk are all open-source or open-architecture products—but the work of integrating them is still considerable. The key decision point is: does your business truly need real-time data synchronization? If the answer is yes, the investment in vertical integration is justified.

One-line Summary

Load Bearing Empire built its own AI infrastructure to unify six business lines, trading operational overhead for real-time data synchronization.

Paragraph Summary

Load Bearing Empire integrated six business lines—real estate wholesaling, demolition, valet parking, credit repair, mineral rights, and a SaaS product—onto a single self-built AI stack. They abandoned SaaS tools like Twilio, SendGrid, and Make.com in favor of Supabase (98 tables, 42 Edge Functions) as a unified data layer, VAPI driving 26 autonomous AI agents, Asterisk 18.10 for the phone system, and a self-built JARVIS Brain Router to orchestrate all inbound workflows. The core logic: when business spans the physical and digital worlds, real-time cross-system data synchronization matters more than saving on operational costs. Self-hosting on Vultr brings integration flexibility and data control, but requires the team to have DevOps capabilities. The case shows that in multi-business-line AI deployment, the depth of infrastructure integration is more critical than the sophistication of individual point tools.