Building Production SaaS
in 7 Days with AI
A systematic approach to AI-augmented development: real commits, real challenges, real solutions. Learn from 1,357 commits and 12 architectural decisions.
Tools & Stack
The right tools for the right job
Core development with Claude Sonnet 4.5 integration
Learn More & Get Started →80% of code generation, pattern design, refactoring
Red-hat reviews, quality audits, documentation
3 simultaneous branches without conflicts
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Day-by-Day Breakdown
How the 7 days unfolded in reality
✅ Achievements
- Next.js 15 + TypeScript setup
- NextAuth v5 with MongoDB
- 67 prompts in TypeScript files
- Shadcn UI components
- Basic routing and navigation
💡 Key Lessons
- Start with TypeScript files, migrate to DB later
- Real auth from day 1 (not toy login)
- Ship working features over perfect architecture
✅ Achievements
- Repository pattern implemented
- AI Provider Factory (5 providers)
- Enterprise RBAC (6 roles, 13 permissions)
- OpsHub admin dashboard
- 91 repository tests
💡 Key Lessons
- Add patterns when APIs stabilize
- Multi-tenant from the start (organizationId)
- Test business logic, not just units
✅ Achievements
- 620 passing tests
- RED metrics (p50/p95/p99)
- PII redaction (GDPR/SOC2)
- Rate limiting + replay protection
- 3 incident playbooks
- 4 ADRs documented
💡 Key Lessons
- Observability is not optional
- Budget enforcement prevents runaway costs
- Document decisions as you make them
✅ Achievements
- Patterns migrated to MongoDB
- 39 critical TODOs resolved
- Real achievements system
- Career recommendations API
- Site stats from database
💡 Key Lessons
- Systematic TODO resolution > random fixes
- Migrate when patterns emerge, not day 1
- Real data builds trust
✅ Achievements
- Mock data removal (|| 76, || 23)
- 12 QA issues fixed
- Pre-commit hooks for compliance
- UI/UX polish (dark mode, readability)
- Red Hat trust audit
💡 Key Lessons
- Pattern audits catch systematic bugs
- Pre-commit hooks prevent regressions
- Fake data destroys credibility
Key Techniques
What made this approach different
Problem
Multiple AI agents causing merge conflicts
Solution
Created 3 separate worktrees (main, DRY improvements, QA polish) enabling simultaneous development
Result
Zero merge conflicts despite 3 parallel branches
Problem
One AI model is not optimal for all tasks
Solution
Claude for architecture, GPT-4 for reviews, Gemini for research
Result
20-30% faster by using model-specific strengths
Problem
Quality regression from rapid AI-assisted development
Solution
Automated checks for mock data, enterprise compliance, schema validation
Result
Caught 15+ issues before they reached production
Problem
Losing context on why decisions were made
Solution
Document architectural decisions in ADR format with context, decision, consequences
Result
12 ADRs created, zero "why did we do this?" questions
By the Numbers
Measurable outcomes from 7 days
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