HoneyBun · AI Orchestration Platform

I built the factory before I scaled the sales.

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.

Andrew Cruz · Founder, HoneyBun · U.S. Marine Corps Recruiter (Ret.) · Chino Hills, CA
5
Live Paying Clients
13
Vertical Templates
39
Specialist Sub-Agents
41k
LOC PWA Screens
12,220
Pages Indexed
$297
/mo Stack Cost
Architecture Hierarchy

Four layers. Conflict-resolution baked in.

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.

LAYER 1 / GLOBAL
Always-On Instincts
Non-negotiable behavior rules. Verify-first. No claim without source read. Stakes-asymmetry beats speed.
LAYER 2 / ECOSYSTEM
Projects-Wide Standards
Quality bar, security, testing, performance, git workflow, ship-gate, CI/CD discipline.
LAYER 3 / PLATFORM
HoneyBun Platform Rules
Multi-tenant patterns, vertical theme system, worker conventions, deploy rules, operator vs. internal split.
LAYER 4 / PROJECT
Per-Repo Context
Workers, dashboard, PWA, themes, clients — each surface gets its own CLAUDE.md with deploy commands and gotchas.
PROTOCOL
Task Board API · Single Source of Truth
Every task, every agent, every session writes to one board. Status flow, orphan sweep, parallel-session race protection.
MEMORY
MemStack · Cross-Session Knowledge
SQLite + semantic vector search. Sessions, decisions, lessons, project context. Karpathy-style LLM vault for institutional memory.
Multi-Agent Governance · Personal AI Dispatch Protocol

39 specialist sub-agents. Six exec-persona reviewers. Six binary quality gates.

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.

plan-checker
Goal-backward plan validation. Max 3 loops.
code-reviewer
No CRITICAL/HIGH lands. Fires on every code change.
security-reviewer
Auth, input, secrets, API. Rotates exposed keys.
tdd-guide
Tests-first enforcement. 80% coverage minimum.
planner
Standard + complex implementation plans.
assumptions-analyzer
Hidden assumptions surfaced with evidence.
advisor-researcher
Gray-area decisions. Parallel × N.
research-synthesizer
Merges parallel research into one brief.
architect
System design, multi-system decisions.
debugger
Root-cause analysis. Stack-trace triage.
api-detective
Failed calls, missing data, OAuth breaks.
database-reviewer
SQL, schema, migrations, performance.
build-error-resolver
Build green with minimal diff.
e2e-runner
Critical-flow verification via browser.
refactor-cleaner
Dead code, knip, depcheck, ts-prune.
doc-updater
Codemaps, READMEs, change docs.
exec-ceo · Bezos
Customer obsession. Day 1. Flywheel.
exec-coo · Cook
Operational precision. Single-thread owner.
exec-cfo · Munger
Inversion. Moats. Unit economics.
exec-cto · Vogels
Failure modes. API-first. Scale-under-load.
exec-cmo · Godin
Tribe. Smallest viable market. Purple cow.
exec-caio · Karpathy
Software 2.0. AI theater detection. Evals.
skeptic
Destruction-test. Severity-rated problems.
strategist
Market intent. Differentiation. Brief.
Factory Pattern

Marginal cost of a new vertical ≈ zero.

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.

PROSPECT
CLONE GOLDEN
DEPLOY LIVE
photo-booth · LIVE
realtor · LIVE
photo-booth · LIVE
photo-booth · LIVE
photo-booth · LIVE
plumber · golden
medspa · golden
gym · golden
barbershop · golden
nail salon · golden
+ 8 more verticals
tenant #50 → same cost as #6
Mobile Cockpit · Two PWAs, One Codebase

The internal app is the AI operations cockpit F500 programs need.

~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.

OPERATOR PWA · CUSTOMER-FACING

operator.gethoneybun.com

  • edit-sections — 8,743 LOC. Self-serve site editing.
  • signal — 2,894 LOC. Inbound lead intelligence.
  • onboarding — 2,162 LOC. Activation flow.
  • conversations · leads · card · dashboard · edit · account
  • Workbox service worker · web push · install prompt · offline.html
INTERNAL PWA · AI OPS COCKPIT

ops.gethoneybun.com

  • leads-platform — 3,325 LOC. Funnel ops surface.
  • pipeline · rank-tracker · errors · intelligence
  • prospect-research · prospect-qualifier · prospect-demo — AI-assisted sales workflow
  • seo-perfector · seo-flywheel · seo-health — autonomous improvement loop
  • aeo-queue · dcc-admin · converter · lead-prefill · verticals · audits · board · assets · inbox · pages
  • 24 internal screens spanning governance, AI workflows, and observability
Reliability Discipline

Silent failure is the cardinal sin.

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.

1 · DETECT
reportFailure()
Client-side hook. Severity tagged. Context attached.
2 · CLIENT RETRY
Retry × 2
0s then 5s delay. Toast if still failing.
3 · WORKER
Auto-Remedy
Health check · dedup · auto-fix task created.
4 · VERIFY
Re-check × 2
5s intervals. Confirm fix or escalate.
5 · ESCALATE
Push · Email · SMS
Named human owner. No failure exits silently.
Operating Cadence

AI-native ops, not AI features bolted on.

AUTONOMOUS LOOP

Cron-Scheduled Agents

  • Morning briefing on session start — overnight autopilot, failures, stale tasks, today's schedule
  • Orphan sweep — stale tasks from dead sessions reclaimed automatically
  • Pre-flight checks — credentials, upstream reachability, no conflicting in-progress work
  • Health indicators — green / yellow / red per scheduled automation, surfaced at session start until resolved
  • Bounded autonomy — never archives human-claimed work; only meta-stale alert noise
LEARNING LOOP

Lessons → Hooks

  • Every failure produces a per-shard lesson at ~/.claude/lessons/
  • Recurring patterns get promoted from probabilistic rules to deterministic hooks
  • 5+ formal post-incident write-ups in the workers repo (circuit breakers, drift retries, timeout handling)
  • 601 lines of structured institutional lessons in honeybun/lessons.md
  • Verify-before-work protocol — workers check completed_at + existing code state before claiming any task. Prevents parallel-session re-do.
Stack

Production infrastructure, not a notebook.

Backend

Cloudflare Workers Supabase Postgres + Vector REST APIs MCP Servers

Frontend

Vite 6 Workbox PWA WordPress (vertical-aware) Elementor

AI

Claude Opus/Sonnet/Haiku OpenAI GPT Gemini Workers AI Custom MCP

Deploy

Vercel Cloudflare Pages Cloudways Railway Wrangler
Proven Outcomes · Live Proof Case

The numbers the autonomous engine produced.

12,220
Pages Indexed
Autonomous content engine; GSC verified.
403%
YoY Click Growth
190% YoY impressions. 110 ranked cities.
18.5%
Revenue Lift
Closed-loop attribution: impression → revenue.
$45k
/yr Displaced Ad Spend
Across 4 SoCal counties on proof-case site.
40%
Appointments AI-Generated
Sammy conv. AI · voice/SMS/chat · 9 functions.
40%
Show Rate
Zero human engagement. Auto qualify · book · confirm.
4 → 1
Headcount Compressed
Analyst + writer + schema architect + dev → one operator.
$297
/month Stack
All-in recurring infra for the entire platform.

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.

ANDREW CRUZ · GETHONEYBUN.COM · LINKEDIN.COM/IN/ANDREWCRUZ101