The Playbook
Every step from raw niche idea to scaled revenue engine — fully systematized, AI-powered at every stage.
Deep Research
Every idea goes through our AI-powered research engine before a single line of code is written. 9 specialized agents run in parallel — scraping Reddit, analyzing competitors, modeling ad economics, and stress-testing feasibility. Ideas that survive get a ROAS score and go-to-market blueprint.
Three Attack Strategies
Predator (101 ideas)
Beat weak incumbents with AI UX
Underserved (163 ideas)
No AI-native solution exists yet
Provisioning (161 ideas)
Automate an expert service at 1/10 cost
Output Per Niche
- 18 independent agent reports (9 agents × 2 rounds)
- 1 gap analysis with 8–15 follow-up tasks
- 8–15 deep dive reports
- Final synthesis with ROAS score (1–10)
- GTM blueprint if verdict is BUILD
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
A swarm of AI agents works in parallel — frontend, backend, AI pipeline, infrastructure, testing — coordinated by a lead agent. This is why 5-day builds are possible.
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
- 6–7 agents claim tasks in parallel
- 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
$2K Ad Test
Every app gets the same controlled test: $2K in ad spend across Meta and Google. AI generates creative variants, monitors performance, and auto-kills 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 Go/Kill Decision
Production and hosting costs are negligible. The only question is: does the marketing math work? ROAS directly answers "Is this a business?"
Why ROAS, Not CPA
- Production & hosting cost ≈ $0 per user
- Team is the only fixed cost — already covered
- ROAS directly answers: "Is this a business?"
- No need for complex LTV projections early on
AI-Driven Decisions
- AI monitors ROAS in real-time across all apps
- Auto-kills 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 = near-zero marginal cost
- Learnings from one app accelerate the next
- Revenue grows as portfolio grows