Your AI prototype works in the demo. Make it work in production.
You built something real with Lovable, Bolt, Replit or Cursor. The demo convinced everyone. Then production arrived: the second user sees the first user's data, a key leaks, a deploy breaks and there is no way back. Doomity does not throw your work away. We keep what the prototype got right and rebuild what it faked. The first step is a Production Readiness Sprint: five days, fixed scope, so you launch knowing exactly what holds and what does not.
Updated: July 2026
- 1 Day 1 Kickoff & access → Evidence inventory
- 2 Day 2 Technical review → Findings with evidence
- 3 Day 3 Risk map → Every issue classified
- 4 Day 4 Options A/B/C → Trade-offs on the table
- 5 Day 5 Executive session → 30/60/90 roadmap
Which five failures show up when an AI prototype meets production?
Almost every AI-generated app Doomity reads fails in the same five places. None of them are visible in a demo, and all of them surface with the second user or the first curious visitor:
- Fake auth. A login screen exists, but the API behind it never checks who is asking.
- Open database rules. Row Level Security is missing or misconfigured, so any logged-in user can read everyone's data.
- Secrets in the frontend. API keys shipped inside the JavaScript bundle, readable by anyone who opens DevTools.
- No rollback. One bad deploy and the app is down until someone rebuilds it by hand.
- No tests. Every change is a coin flip on the flows that make you money.
Want to know which of the five your app has? The production scorecard tells you in ten questions.
How insecure is AI-generated code, according to the actual data?
About 45% of AI-generated code introduces security vulnerabilities. That figure comes from Veracode 2025, which tested over 100 large language models on real coding tasks: 86% of samples failed to defend against cross-site scripting and 88% against log injection. The models did not improve on security as they improved at syntax.
This is not an argument against the tools. It is an argument against launching their output unread. Doomity's position is blunt: most vibe-coded prototypes should not go to production as they are, and some should not go at all. The audit tells you which one you have, with evidence, before your users find out for you.
Do you have to throw the prototype away and rewrite everything?
No. In most prototypes the audit keeps more than it rebuilds. The screens, the flows and the business rules are the part you validated with real people, and they are usually worth keeping. What gets rebuilt is the part the AI faked because nobody was checking:
| Layer | What the audit usually decides |
|---|---|
| UI and screens | Kept. This is what the prototype got right. |
| User flows and business logic | Kept, with tests added around the critical paths. |
| Authentication and permissions | Rebuilt on a proven provider, enforced server-side. |
| Database rules (RLS) | Rewritten and verified table by table. |
| Secrets and configuration | Moved server-side and rotated. |
| Deploys, CI and rollback | Set up from scratch. |
A full rewrite is the exception, and it has to be earned with evidence from the audit, not asserted on day one to pad an invoice.
What happens during the five days of the Production Readiness Sprint?
The Production Readiness Sprint is Doomity's fixed-scope first engagement: five days, defined deliverables, no long-term contract. It runs in the same order every time:
- Day 1 — Audit. Senior engineers read the code, the database rules, the dependencies and the hosting setup. No juniors, no automated report dressed up as an audit.
- Day 2 — Risk map. Every finding classified: blocks launch, fix after launch, or fine as it is.
- Day 3 — Plan. A hardening plan scoped with you, so you approve the work before it starts.
- Days 4–5 — Hardening. The launch blockers get fixed: auth, data rules, secrets, rollback, smoke tests on the flows that matter.
You end the week with the risk map, the hardened blockers and a launch checklist you can execute with Doomity or with anyone else. The guarantee covers process and deliverables, never results: anyone promising outcomes on code they have not read is guessing. The full delivery process is documented in the Doomity method.
Which AI builders and vibe-coding tools does Doomity work with?
Lovable, Bolt, Replit, Cursor, Base44, v0. The list matters less than it seems, because these tools converge on similar output: a React or Next.js frontend, a Supabase or Firebase backend, and the same five failure points listed above. Doomity audits the code the tool produced, not the tool itself.
Built with something else, or with a mix of tools and hand-written code? Still fine. Reading unfamiliar code fast is the core skill here, and it transfers. If the codebase can be read, it can be audited and hardened.
What about the personal data already sitting inside your prototype?
By the time a prototype has fans, it usually has real personal data: emails, names, sometimes payment details, often collected before anyone thought about GDPR. That data may be sitting in a database with open rules, hosted in a region you never chose, behind accounts in one founder's personal email.
The audit maps this explicitly: what personal data exists, where it is stored, who can access it, and what GDPR requires you to fix before scale makes it expensive. Doomity works under NDA, through accounts in your name, and can move hosting to EU regions when the audit calls for it. Prefer to ask about any of this first? Book a 25-minute call.
Who is Doomity, and why trust them with your prototype?
Doomity LLC is a software development firm specializing in custom software, legacy modernization, AI prototype-to-production, software project rescue and technical due diligence, serving clients in the US, UK, Spain and Portugal. Its engineers come from teams that have built for Holcim, Canon España and Indra.
That mix is the point. Prototype-to-production sits between two worlds: the speed of AI tools and the discipline of enterprise delivery. Doomity uses AI in its own process, up to 40% faster, with every finding checked by a senior engineer. And if your app is already live and breaking, that is a different problem with a different first step: software project rescue.
FAQ
It means a specific, checkable list, not a feeling. Auth enforced on the server, not just in the UI. Database rules verified table by table. Secrets stored server-side and rotated. A rollback path, so a bad deploy is a five-minute incident instead of a lost weekend. Monitoring that tells you something broke before a user does. And tests on the flows that make you money. The Production Readiness Sprint measures your app against that list and hardens what fails.
Usually not. The audit keeps the UI, the user flows and the business logic you validated with real users, and rebuilds authentication, data rules and infrastructure where the prototype faked them. A full rewrite is the exception and has to be justified with evidence from the audit, line by line if needed. A vendor who proposes a rewrite before reading your code is protecting their own comfort, not your launch date.
Not by default. Veracode's 2025 study of over 100 language models found roughly 45% of AI-generated code introduces security vulnerabilities, with cross-site scripting and log injection failing most often. The practical stance: treat AI output like code from a fast, confident junior who never had a review. The tools are genuinely useful. Shipping their output unread to real users is the mistake, and it is the exact mistake the audit exists to catch.
The Production Readiness Sprint is five days, fixed scope, and that part does not move. By day 3 you hold a hardening plan; by day 5 the launch blockers are fixed and you have a launch checklist. Whether more work follows depends on what the audit finds: some apps launch straight after the Sprint, others need a scoped second phase for the fix-after-launch items. You will know which case you are in before committing to anything.
Lovable, Bolt, Replit, Cursor, Base44 and v0 are the ones Doomity sees most, but the audit works on code, not logos. These builders converge on similar stacks (React or Next.js frontends, Supabase or Firebase backends) and on the same failure points: fake auth, open database rules, secrets in the frontend, no rollback, no tests. If your prototype mixes tools, or mixes AI output with hand-written code, it can still be audited.
Two separate questions, both covered. Your codebase: Doomity works under NDA, through accounts in your name, and you keep owner access at all times. Your users' data: the audit produces a data exposure map showing what personal data the prototype holds, where it is hosted, who can reach it, and what GDPR requires before you scale. If hosting needs to move to an EU region, that goes into the hardening plan with the rest.
Is your AI-built app production-ready? 10-question check
Ten questions, two minutes. You get a score, a risk band and the three fixes to make before real users arrive — before giving us your email.
Question 1 of 10
Where do we send your result?
Work email. Your score and actions appear right here; no newsletter.
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Every week an unhardened prototype runs in front of real users is a week of open exposure: one leaked key or one open table can cost more than the entire hardening ever would. The Production Readiness Sprint takes five days and ends with your app measurably safer, whatever you decide next. Book it before your users audit the app for you.