Built a mvp / prototype with Warp AI?
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 Warp AI apps
Building a mvp / prototype with Warp AI 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 Warp AI mvp / prototype
Common Warp AI issues we fix
Beyond mvp / prototype-specific issues, these are Warp AI patterns we commonly fix.
Generated shell commands may have destructive side effects
Warp AI generates commands that work for the described task but may have unintended side effects — deleting files, overwriting configurations, or modifying system state in ways not specified.
Infrastructure commands executed without dry-run validation
Terraform, kubectl, and cloud CLI commands generated by Warp AI may be run directly without a plan or dry-run step, risking unintended infrastructure changes in production environments.
Scripts lack error handling and exit codes
Generated Bash scripts often don't include `set -euo pipefail`, error trapping, or meaningful exit codes. Silent failures in CI/CD pipelines can cause partial deployments that appear successful.
Generated Dockerfiles not optimized for layer caching
Warp AI generates functional Dockerfiles but doesn't order commands to maximize layer cache reuse, resulting in slow builds that reinstall all dependencies on every code change.
Start with a self-serve audit
Get a professional review of your Warp AI mvp / prototype at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code 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 Warp AI?
Warp AI 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 Warp AI leave in mvp / prototype apps?
Common issues include: generated shell commands may have destructive side effects, infrastructure commands executed without dry-run validation, scripts lack error handling and exit codes. For a mvp / prototype specifically, these issues are compounded by the need for identifying what actually needs fixing.
How do I make my Warp AI mvp / prototype production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Warp AI-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.
How much does it cost to fix a Warp AI-built mvp / prototype?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger mvp / prototype projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Warp AI mvp / prototype production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.