Launch Checklist
Before you launch: audit and prepare your AI-built app for production.
You shipped fast. Cursor, Lovable, Bolt, Replit, or some combination of modern AI tools helped you build in weeks what used to take months. The app works. Users are clicking around. Now comes the question every AI-assisted founder eventually asks: is this actually ready?
The honest answer is: probably not yet. Not because you did something wrong, but because AI coding tools optimize for speed and functional correctness. Production requires a different checklist.
Veracode's 2025 GenAI Code Security Report found that AI-generated code introduced risky security flaws in 45% of tests and that larger, newer models did not automatically improve security outcomes. The takeaway for founders is practical: a working app still needs a deliberate audit before production.
Use this in order
The audit comes first. There is no point tuning deployment pipelines or scaling infrastructure around a codebase with unresolved security holes, unverified dependencies, or broken business rules. Once the codebase is understood and the launch-blocking risks are addressed, the production-readiness checklist tells you what "ready" needs to include.
Part 1: audit the AI-generated codebase
A code review asks whether the app works. A production audit asks what happens when it fails, gets attacked, or scales beyond the demo scenario.
Security vulnerabilities
Start with security because deployment polish does not help if the code has unresolved high-risk flaws. Run a SAST scan across the whole codebase and treat critical or high findings as launch blockers.
- Run Semgrep, Snyk Code, Veracode, or another SAST tool against the full app.
- Review user input handling for XSS, injection, path traversal, and unsafe deserialization.
- Check authentication and authorization flows on the server, not only in the UI.
- Verify security headers, CSRF protection, session handling, and password reset flows.
Hardcoded secrets
AI-generated prototypes often include example credentials, pasted API keys, or connection strings that survive longer than anyone expects.
- Scan the current code and full git history with GitLeaks, TruffleHog, GitGuardian, or similar tooling.
- Move secrets into environment variables and a managed secrets store.
- Rotate every exposed credential, even if the app appears to be working.
- Add pre-commit and CI checks that block new secrets before they land.
Technical debt and duplicated logic
AI-assisted codebases often degrade through iteration. The app may work, but validation, errors, data access, and permissions can be implemented differently across the system.
- Find duplicated validation, auth, database access, and formatting logic.
- Standardize error handling so routes do not mix thrown exceptions, null returns, and silent failures.
- Remove dead code, unused dependencies, commented-out experiments, and orphaned helper functions.
- Run static analysis with tools such as SonarQube or the equivalent in your stack.
Dependency and package risk
Package suggestions from AI tools should be treated as untrusted until verified. Hallucinated package names, abandoned packages, and vulnerable transitive dependencies all matter before launch.
- Verify every dependency exists in the real package registry and has credible usage history.
- Run SCA tooling such as Snyk Open Source, Dependabot, or OWASP Dependency-Check.
- Commit lockfiles and review loose version ranges before production builds.
- Remove packages that were scaffolded but are not actually used.
Business logic and edge cases
The happy path is not enough. Production users submit malformed input, retry actions, open multiple tabs, cancel midway, and trigger race conditions the demo never exercised.
- Write adversarial tests for empty values, very long values, malformed payloads, and direct API calls.
- Test concurrent writes, duplicate submissions, webhook ordering, and retry behavior.
- Check assumptions about missing records, deleted users, expired sessions, and partially completed onboarding.
- Assign owners for known logic gaps before the production checklist begins.
Audit checkpoint
Before moving to production readiness, you should have a completed SAST scan, all critical and high findings addressed, secrets scanned and rotated where needed, dependencies verified, and known logic gaps assigned to owners. Anything unresolved here creates a false sense of launch readiness later.
Part 2: prepare the production system
The audit tells you what is broken. This checklist tells you what production needs before real users, real data, and real failures enter the system.
Architecture and data layer
Before launch, make the implicit architecture explicit. Know how data moves, which services own which responsibilities, and what happens when core dependencies fail.
- Configure database connection pooling and confirm connection limits under load.
- Use managed, versioned migrations rather than manual production SQL changes.
- Encrypt sensitive data in transit and at rest, including file and object storage.
- Automate backups and run at least one restore test before launch.
- Review database users, IAM roles, API scopes, and service keys for least privilege.
Scaling and performance
The real scaling question is not whether the app can handle more users. It is what breaks first, at what threshold, and whether you know before customers do.
- Load test staging with expected peak traffic and a higher stress scenario.
- Identify slow queries, missing indexes, and N+1 patterns.
- Move email, file processing, AI calls, and other long-running work into background jobs.
- Confirm hosting and worker tiers can scale horizontally or have a clear upgrade path.
- Configure dead-letter queues for background jobs that cannot complete safely.
Deployment and release process
A production app needs a repeatable release process. If launch depends on a manual command and a lucky deploy, the process is not ready.
- Create a staging environment that mirrors production closely enough to catch real issues.
- Run tests, lint, type checks, SAST, and dependency scans in CI before deployment.
- Manage production environment variables through a secure process, not copied local files.
- Define a rollback path that can be executed quickly when a release fails.
- Document the exact steps required to ship, verify, and revert a release.
Observability and incident response
You cannot operate what you cannot see. Install the basic signals before launch, not after the first user reports a broken workflow.
- Install error tracking such as Sentry, Rollbar, or an equivalent provider.
- Replace casual console logging with structured logs that include request IDs.
- Add uptime monitoring from outside your infrastructure.
- Track p95 and p99 latency, slow queries, queue depth, and background job failures.
- Create a simple runbook covering who responds, how users are updated, and how rollback happens.
Launch with confidence, not just speed
The tools that let you build faster have not changed what production requires. A system handling real user data, real transactions, and real concurrent load still needs secure code, a reliable data layer, repeatable deployment, and enough visibility to know when something goes wrong.
The gap between "it works on my machine" and "it works in production" is where founders most often lose time. Run the audit first. Close the findings. Then work through the production checklist.
Next Step
If you want an experienced read on your specific app, start with discovery.
ScaleProof Labs works with founders who built with AI tools and need a senior technical perspective before they go live. We can help identify the highest-priority risks and turn them into a practical remediation plan.