13 pre-built vertical templates. A clone-and-deploy pipeline. Five live paying customers proving it runs. Customer #50 ships as fast as customer #6.
Context files load bottom-up. Project specificity wins. When ambiguous, the more restrictive constraint applies. The pattern most enterprise AI programs build by year three — designed in on day one.
A custom 270-line dispatch router I built and apply to every project I run. Every task is classified (trivial / standard / complex), routed through the correct pipeline, and gated through Plan · Code · Security · Test · Build · Business. No gate may be skipped. A RED verdict from a relevant executive persona blocks execution.
Templates ≠ tenants. The 13 golden apps are the factory; the 5 live customers are forks of the factory line. Each carries its own git SHA and deployment timestamp.
~41,000 lines of screens sharing 21 cross-app modules. Customer-facing PWA and internal AI ops console deploy from one source. No App Store review cycles.
Every catch block routes through reportFailure(). Auto-remedy attempted first. Triple-channel escalation if it can't self-heal. Named human owner on every alert.
~/.claude/lessons/honeybun/lessons.mdcompleted_at + existing code state before claiming any task. Prevents parallel-session re-do.The hard part of enterprise AI was never the technology. It was always going to be getting people to want to move with you. Eleven years as a Marine Corps career recruiter taught me to operate that way. Three years building HoneyBun proved the operating model holds at machine scale, too.