How to move from Lovable to Production

Lovable gets you from zero to prototype fast. But once you have paying users and a roadmap, you realize that you have no type safety, no tests, no CI/CD. Here's how to fix it.

Why migrate?

Lovable generates code that works, but it optimizes for speed of creation, not long-term maintainability:

• No type safety: everything is any or loosely typed, so bugs slip through silently
• No tests: every new feature risks breaking the last one
• Monolithic structure: all logic crammed into a few large files
• No CI/CD: deployments are manual and error-prone
• Hardcoded config: environment-specific values baked into the code

Step 1: Set up version control properly

If you exported your Lovable project, you likely have a flat directory of files. Start by initializing a proper Git repository with a clean commit history.

git init
git add .
git commit -m "Initial export from Lovable"

Step 2: Add TypeScript strict mode

Lovable projects often use TypeScript, but with loose settings. Tighten the configuration:

{
  "compilerOptions": {
    "strict": true,
    "noUncheckedIndexedAccess": true
  }
}

You'll get 100+ errors. That's good. Each error is a production bug you're catching instead of your users.

Step 3: Extract and structure

Break apart the large files.

• One component per file: max 150 lines
• Separate data fetching from rendering: use server components or hooks
• Group by feature, not by type: features/billing/ instead of components/, hooks/, utils/

# Before (typical Lovable export)
src/
App.tsx          # 800 lines, routing + state + UI
utils.ts         # grab-bag of helpers
api.ts           # all API calls in one file

# After (structured)

src/
features/
billing/
billing-form.tsx
billing-api.ts
billing.test.ts
dashboard/
dashboard-page.tsx
dashboard-widgets.tsx

Step 4: Add tests for critical code

You don't need 100% coverage. Start with the paths that would hurt most if they broke:

• Authentication flow
• Payment processing
• Core business logic

test("billing calculates pro-rated amount correctly", () => {
  const result = calculateProration({
    plan: "pro",
    daysRemaining: 15,
    totalDays: 30,
  });
  expect(result.amount).toBe(4950); // $49.50 in cents
});

Step 5: Set up CI/CD

Automate the guardrails. Every change should run:

1. Type checking (tsc --noEmit)
2. Linting (eslint)
3. Tests (vitest run)
4. Build verification (next build)
5. AI code review, for example Greptile or CodeRabbit

name: CI
on: [push, pull_request]
jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: pnpm install --frozen-lockfile
      - run: pnpm typecheck
      - run: pnpm test
      - run: pnpm build

Step 6: Infrastructure as code

Replace manual deployments with declarative infrastructure. Tools like Pulumi or Terraform let you define your entire stack in code:

const api = new aws.lambda.Function("api", {
  runtime: "nodejs20.x",
  handler: "index.handler",
  code: new pulumi.asset.AssetArchive({
    ".": new pulumi.asset.FileArchive("./dist"),
  }),
});

Now deployments are reproducible, reviewable, and reversible. For a deeper dive, see Infrastructure as Code and Vibe Coding.

The result

After migration, your team can:

• Ship features faster with AI tools (Cursor, Claude Code) because the guardrails catch mistakes
• Onboard new developers without "don't touch that file" warnings
• Deploy on Friday and go home

This is how you get from prototype to your first 1,000 paying users.