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Load Bearing Empire runs six interconnected businesses—real estate wholesaling, demolition, valet logistics, credit repair, mineral rights, and structural SaaS—on a single AI infrastructure layer costing $32-45/month in API calls. This isn't a case study in bootstrapping; it's a technical blueprint. We're documenting how Domonique Luchin architected a 4-class memory system on Supabase, integrated Claude Sonnet as a reasoning backbone with VAPI for voice automation, layered Asterisk PBX for call routing, and implemented a 15-class failure taxonomy to keep operational costs deterministic across 6 revenue streams. This article breaks down the actual implementation: database schema decisions that let one backend serve demolition dispatch, real estate lead qualification, and SaaS billing simultaneously; why self-hosted Asterisk reduced per-call costs by 73%; how token budgeting works when you're running AI agents across voice, SMS, and API channels; and the Telegram approval gate that prevents runaway spending. For engineers building cost-efficient, multi-tenant AI systems—especially those bootstrapping from nothing—this is the technical foundation.
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