The Playbook
The operating loop behind the factory: select ideas, build with agents, launch paid tests, measure unit economics, and feed the learning back into the next decision.
Selection Engine
Every idea is evaluated before a single line of code is written. The dashboard ranks opportunities by buyer, market size, users, build days, blockers, LTV/CAC, and strategic fit.
Three Attack Strategies
Predator
Beat weak incumbents with AI UX
Underserved
No AI-native solution exists yet
Provisioning
Automate an expert service at 1/10 cost
Output Per Niche
- Structured research evidence
- Weighted score and rank
- Known blockers before build
- GTM assumptions before ad spend
- Lifecycle status from brainstorm to scale
AI-Native Spec
Research output feeds directly into a detailed technical specification. Every spec is designed to be executed by AI build agents — with explicit work packages, acceptance criteria, and architecture decisions.
What the Spec Includes
- Product overview with target personas
- Feature list with priority tiers (MVP / Post-MVP)
- AI assistant personality and behavior spec
- Data model and API contract
- Work packages (parallelizable units)
Quality Criteria
- Every feature has acceptance criteria
- AI interactions are specified with examples
- Stripe tier pricing defined
- PWA behavior documented
- Spec passes agent readability review
5-Day Build
Specialized agents work across frontend, backend, AI pipeline, infrastructure, testing, deployment, support, ads, and investor materials. A founder-owned review loop keeps judgment in the system.
Every App Includes
- Embedded AI assistant (streaming responses)
- Stripe subscription (3-tier pricing)
- PWA with offline support + install prompts
- Firebase auth (Google, email, anonymous)
- Mobile-first responsive design
Build Process
- Lead agent reads spec, creates work packages
- Specialized agents claim scoped tasks
- Continuous integration via shared task list
- Automated testing throughout
- Human review on Day 4–5
Single-Command Deploy
One command provisions the entire production stack. No manual setup, no configuration drift, no forgotten steps.
GCP Project
Dedicated project with billing, IAM, and service accounts configured
Cloud SQL
PostgreSQL 15 instance with database, user, and connection configured
DNS + SSL
Custom domain mapped, SSL certificate provisioned automatically
Firebase
Auth providers, Firestore rules, and admin SDK configured
Stripe
Products, prices, and webhooks created for 3-tier pricing
Cloud Run
Deployed with min-instances=1 to eliminate cold starts
$3K/mo Ad Test
Every app gets a controlled market test: ~$3K/month in ad spend across Meta and Google for 1–3 months. Enough volume for statistically sound unit economics decisions. AI generates creative variants, scores them with a vision model, and pauses underperformers.
Optimization Cadence
Ad Infrastructure
- Facebook Pixel + Conversions API (CAPI)
- GA4 event tracking for full funnel
- AI-generated ad creative variants
- A/B test: hooks, benefits, urgency angles
Real-Time Market Data
- Google Trends API for demand signals
- Keyword Planner for CPC + volume
- Competitor ad library monitoring
- ROAS tracked in real-time across all apps
ROAS Scale/Stop Decision
Build speed is useful only when it is tied to distribution data. ROAS, activation, retention, and objections decide whether the factory improves, pauses, or scales.
Why ROAS, Not CPA
- CAC and payback matter earlier than beautiful product demos
- ROAS shows whether paid distribution can work
- Activation and retention explain why it works or fails
- Support and objections feed product decisions
AI-Driven Decisions
- AI monitors ROAS in real-time across all apps
- AI pauses underperforming ad sets at Day 4/7/10
- AI generates creative variants to push ROAS up
- Framework is fully automated — humans set thresholds
Scale & Compound
Graduated apps enter an automated improvement cycle: user feedback drives AI app improvements, which drive higher conversion, which drives higher ROAS, which justifies more ad spend.
Automated Improvement Loop
- AI generates + tests ad creative variants
- User feedback collected → AI improves app
- Better app → better conversion → higher ROAS
- Higher ROAS → more budget → more users
- Entire loop runs with minimal human input
Portfolio Compounds
- Each graduated app is a self-sustaining revenue engine
- Diversification across niches reduces risk
- Shared infrastructure reduces the marginal cost of each new product
- Learnings from one app accelerate the next
- Revenue grows as portfolio grows