Built a mvp / prototype with GPT Engineer?
We'll make it production-ready.
You built a prototype with an AI tool and it works — on your machine, with your test data, for the happy path. Now you need real users to validate your idea. The goal isn't perfection — it's getting to a state where real people can use it safely and reliably. That means fixing the critical gaps without over-engineering.
MVP / Prototype challenges in GPT Engineer apps
Building a mvp / prototype with GPT Engineer is a great start — but these challenges need attention before launch.
Identifying what actually needs fixing
Not everything needs to be production-grade for an MVP. The challenge is knowing which shortcuts are acceptable (visual polish, advanced features) and which aren't (security, data loss, broken core flows). Our audit prioritizes fixes by impact.
Security minimum
Even an MVP handles user data. You need authentication that works, data isolated per user, no exposed secrets, and HTTPS. You don't need enterprise-grade security, but you need the basics done right.
Deployment
Your app runs locally but deploying it involves environment variables, domain configuration, SSL, and build optimization. First-time deployment is where most AI prototypes hit unexpected issues.
Error handling
When something goes wrong (and it will), your users should see a helpful message — not a white screen or a raw error. Basic error handling is the difference between 'this app is buggy' and 'something went wrong, please try again.'
Core flow reliability
Your main user flow (signup → core action → outcome) must work every time. Edge cases in secondary flows can wait, but the core path needs to be solid for your MVP to generate valid learnings.
What we check in your GPT Engineer mvp / prototype
Common GPT Engineer issues we fix
Beyond mvp / prototype-specific issues, these are GPT Engineer patterns we commonly fix.
No authentication system
GPT Engineer generates functional UIs but typically skips authentication entirely. All routes and data are publicly accessible.
Direct database access from client
Some generated apps query databases directly from the frontend without an API layer, exposing database credentials and structure.
Incomplete feature implementations
Features that look complete in the UI but don't actually work end-to-end. Buttons that don't submit, forms that don't save, and links that go nowhere.
Missing error boundaries
A single component error crashes the entire application. No error boundaries or fallback UIs to gracefully handle failures.
Start with a self-serve audit
Get a professional review of your GPT Engineer mvp / prototype at a fixed price.
Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 checks
- SSL/TLS analysis
- Security headers
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerabilities
- Code quality review
- Dependency audit
- AI pattern analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
Frequently asked questions
Can I build a mvp / prototype with GPT Engineer?
GPT Engineer is a great starting point for a mvp / prototype. It handles the initial scaffolding well, but mvp / prototype apps have specific requirements — identifying what actually needs fixing and security minimum — that need professional attention before launch.
What issues does GPT Engineer leave in mvp / prototype apps?
Common issues include: no authentication system, direct database access from client, incomplete feature implementations. For a mvp / prototype specifically, these issues are compounded by the need for identifying what actually needs fixing.
How do I make my GPT Engineer mvp / prototype production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most GPT Engineer-built mvp / prototype apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
Get your GPT Engineer mvp / prototype production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.