I Shipped a SaaS MVP in 14 Days Using AI Coding Agents

TL;DR: As a solo developer, I built and launched a SaaS MVP in two weeks using AI coding agents (Claude Code, Cursor, and GitHub Copilot). Here's the day-by-day breakdown of what I built, what the AI handled versus what I coded myself, and the real costs involved.

---

The Project

I wanted to build InvoiceFlow — a simple invoicing and billing dashboard for freelancers. The core features: create invoices, track payments, send payment reminders via email, and basic analytics. Nothing revolutionary, but something I could actually ship and charge for.

Tech stack: Next.js (frontend), Laravel (API), PostgreSQL, Stripe, and SendGrid.

Here's how it went.

---

Day 1-2: Planning and Setup

What I did:

• Wrote a 2-page spec document
• Designed the database schema on paper
• Set up GitHub repository
• Configured Next.js with TypeScript and Tailwind
• Bootstrapped Laravel API with Sanctum for auth

What the AI helped with:

• Cursor generated the initial Laravel project structure with migrations
• Copilot suggested the TypeScript types I'd need based on my spec

What I coded manually:

• The actual database schema (migrations)
• Auth flow logic (Sanctum tokens, middleware)
• Project architecture decisions

Time spent: ~6 hours total

---

Day 3-4: Database and Core Models

What I did:

• Created all Laravel migrations (users, clients, invoices, line items, payments)
• Set up Eloquent models with relationships
• Created Laravel Factories for testing data
• Wrote the base API structure

What the AI handled (Claude Code):

# I prompted: "Create a migration for invoices table with client_id, status, due_date, total_amount, notes"

Claude generated the full migration file with proper column types, indexes, and foreign key constraints.

# Then: "Create an Invoice model with relationships to Client and Payment"

Output: A complete Eloquent model with all relationships, casts, and query scopes.

# Finally: "Create factories for User, Client, Invoice, and Payment models"

Got back 4 factory files with realistic fake data generation.

What I coded manually:

• Custom query scopes (e.g., scopeOverdue(), scopePaid())
• Model accessors and mutators for calculated fields
• The actual business logic that would be hard for AI to infer

Time spent: ~4 hours

---

Day 5-6: Laravel API Endpoints

What I did:

• Built CRUD endpoints for clients, invoices, and payments
• Implemented invoice status transitions (draft → sent → paid)
• Added validation rules for all inputs
• Set up API rate limiting

What the AI handled:

This was where AI shined. I'd write a prompt like:

Create a full REST API controller for invoices with:
- index: paginate by user with filters for status and date range
- store: validate and create with line items in a transaction
- show: return invoice with client and payments
- update: allow status changes and note updates
- destroy: soft delete only for draft invoices

Claude Code generated:

// A complete 200-line controller with:
// - Form request validation classes
// - Proper HTTP status codes
// - Transaction wrapping for data integrity
// - Resource classes for consistent JSON responses
// - Error handling with proper messages

I did this for 5 different controllers. The AI saved me at least 10 hours of boilerplate.

What I coded manually:

• Complex business rules (e.g., "Can't change status from 'paid' to 'sent'")
• Edge case handling
• The actual API route definitions

Time spent: ~5 hours

---

Day 7-8: Next.js Frontend Setup

What I did:

• Set up React Query for data fetching
• Created authentication context and login form
• Built the main dashboard layout with sidebar navigation
• Set up routing with Next.js App Router

What the AI handled (Cursor):

Cursor's inline completion was perfect here. As I typed:

const fetchInvoices = async () => {
  const response = await fetch('/api/invoices')
  const data = await response.json()
  return data.data
}

Cursor suggested:

const { data: invoices, isLoading, error, refetch } = useQuery({
  queryKey: ['invoices'],
  queryFn: fetchInvoices,
  refetchOnWindowFocus: false,
})

It learned my patterns and consistently suggested the right React Query hooks.

What I coded manually:

• The overall component architecture
• State management decisions
• UI/UX flow and design system

Time spent: ~6 hours

---

Day 9-10: Invoice Builder UI

What I did:

• Built the invoice creation form
• Implemented dynamic line item addition/removal
• Added real-time total calculation
• Created client selector with search

What the AI handled:

I'd describe the UI component and Cursor would generate the boilerplate:

Create a form component with fields: client_id (select), due_date (date picker), 
line_items (dynamic array with description, quantity, rate), notes (textarea)

Got back a complete form with:

• React Hook Form integration
• Zod validation schema
• Dynamic field arrays for line items
• Auto-calculation for line item totals

What I coded manually:

• The visual design and Tailwind classes
• User experience details (e.g., focus states, error placement)
• Real-time calculation logic (total = Σ qty × rate)

Time spent: ~5 hours

---

Day 11: Stripe Integration

What I did:

• Set up Stripe Checkout for invoice payments
• Created Stripe webhook endpoints
• Handled payment success/failure events
• Updated invoice status on successful payment

What the AI handled:

Create a Stripe Checkout session controller that:
- Takes invoice_id as input
- Creates a checkout session with line items from the invoice
- Returns the checkout URL

Claude generated the full controller with proper error handling and Stripe configuration.

For webhooks:

Handle Stripe checkout.session.completed webhook:
- Verify signature
- Find the invoice by metadata.invoice_id
- Mark invoice as paid
- Create a payment record

Got webhook verification, event parsing, and database updates in one go.

What I coded manually:

• Stripe account setup and API key configuration
• Webhook signature verification (tricky to get right)
• Error handling for edge cases (duplicate webhooks)

Time spent: ~3 hours

---

Day 12: Email Notifications

What I did:

• Set up SendGrid for transactional emails
• Created email templates for invoice delivery and payment reminders
• Built a queued job system for email sending
• Scheduled daily reminders for overdue invoices

What the AI handled:

Create a Laravel mailable for invoice delivery with:
- Client name and company
- Invoice number, due date, total amount
- A "Pay Now" button linking to Stripe checkout
- Clean HTML template

Claude generated the mailable class and Blade template. Then:

Create a scheduled command that:
- Finds all overdue invoices (status = sent, due_date < today)
- Sends a reminder email to each client
- Logs sent reminders to avoid duplicates

Got back a console command with proper scheduling and query logic.

What I coded manually:

• SendGrid account setup and API configuration
• Email template design (HTML + CSS)
• The actual scheduling in Laravel's console kernel

Time spent: ~3 hours

---

Day 13: Polish and Bug Fixes

What I did:

• Fixed bugs discovered during testing
• Added loading states and error messages
• Improved mobile responsiveness
• Added basic analytics (total invoiced, total paid, outstanding)

What the AI handled:

Copilot was great for quick fixes. I'd notice a bug and start typing the fix, and it would complete it. For example, when invoice totals weren't updating after line item changes, Copilot suggested the useEffect dependency array fix.

What I coded manually:

• All debugging and bug fixes (AI can't debug what it doesn't see)
• UX polish (animations, transitions, hover states)
• Analytics queries and chart components

Time spent: ~6 hours

---

Day 14: Deployment and Launch

What I did:

• Set up Vercel for Next.js frontend
• Deployed Laravel API to a VPS with Forge
• Configured environment variables
• Set up SSL and custom domain
• Wrote documentation and onboarding guide
• Created a landing page with pricing

What the AI handled:

Not much here. Deployment is mostly infrastructure work that AI can't do. I did use Copilot for generating the .env.example file and some Docker configuration snippets.

What I coded manually:

• All deployment configuration
• DNS and SSL setup
• The landing page copy and design
• Documentation

Time spent: ~8 hours

---

What AI Actually Coded vs. What I Coded

AI's Contributions (≈60% of code):

My Contributions (≈40% of code, but 100% of decisions):

---

Cost Breakdown

So I spent about $57/month in recurring costs to build and run the MVP. The AI tools paid for themselves on day 3.

---

What Went Well

1. AI for boilerplate: Controllers, models, forms — AI generated these instantly
2. Consistent code style: AI followed my patterns after learning them
3. Quick iteration: I could prototype features in minutes, not hours
4. Documentation: AI wrote doc comments and READMEs I wouldn't have bothered with

---

What Didn't Work

1. Complex business logic: AI struggled with nuanced rules (e.g., "Only remind clients who haven't been reminded in 7 days")
2. Debugging: AI can't debug errors it can't see. I had to debug integration issues myself
3. UI polish: AI generated functional UI, but the "delight" came from my manual refinement
4. Deployment: Infrastructure is still a manual job

---

The Honest Take

Could I have built this in 14 days without AI? No way. It would have taken me 4-6 weeks, and I probably would have burned out halfway through.

Did AI replace me? No. I still made every architecture decision, handled all debugging, and polished the UX. AI was a force multiplier — it handled the boring stuff so I could focus on the hard stuff.

Verdict: AI coding agents don't replace developers. They make solo development viable. I shipped because I didn't get stuck writing boilerplate. I shipped because AI let me iterate fast enough that momentum carried me through to day 14.

If you're building a side project as a solo dev, get Cursor, Claude Code, or Copilot. Use them aggressively. They won't make you a 10x developer, but they'll make your 1x work go 3-5x faster. And sometimes, speed is the difference between shipping and not shipping at all.