Key takeaways
ORI’s 30-second summary
- AI builders are excellent for validating an idea quickly — the problems start when the prototype becomes the product.
- The usual gaps are security, data integrity, scalability, testing and maintainability.
- A short technical audit tells you whether to harden, partly rebuild or start a clean version alongside.
- Fix authentication, data access rules and secrets first; everything else can follow in priority order.
Vibe coding — describing what you want to an AI tool and letting it generate the app — has become one of the fastest ways to test a startup idea. Founders who could never have afforded a development team are now putting real products in front of real users within days.
That is genuinely good news. But we increasingly meet founders at the same moment: the prototype worked, people are signing up, maybe an investor is interested — and the app is starting to break in ways nobody fully understands.
Why prototypes struggle in production
AI builders optimise for something that works right now in the demo. Production software has to keep working for thousands of users, under attack, for years. The gaps tend to be the same every time:
- Security: API keys in the frontend, missing authorisation checks, and database rules that let any user read everyone's data.
- Data integrity: no validation, no migrations and no backups, so one bad deploy can corrupt or lose records.
- Scalability: queries that load entire tables, no pagination and heavy work done in the browser.
- Testing: little or no automated testing, so every change risks breaking something else.
- Maintainability: duplicated logic and inconsistent patterns, because each prompt generated its own approach.
The most common critical issue we find is not a bug — it is a database that any signed-in user can read in full. Check your access rules before anything else.
Harden, partly rebuild, or start clean?
Not every AI-built product needs a rewrite. A short technical audit — usually a few days — answers the question with evidence instead of opinion.
Harden it
Choose this when the data model is sound and the code is reasonably organised. You fix security, add tests and monitoring, and refactor the worst areas while continuing to ship features.
Rebuild the core
Choose this when the interface is good but the backend and data layer are fragile. Keep the screens users already like and replace what sits underneath, one module at a time.
Start a clean version alongside
Choose this when the prototype cannot be secured or scaled without touching almost everything. The prototype keeps running and keeps learning while the production version is built properly, then users are migrated.
A priority order that works
Whichever route you choose, fix things in order of the damage they could do:
- Move secrets to the server, rotate any key that was ever exposed, and lock down data access rules.
- Add server-side validation and authorisation to every endpoint.
- Set up automated backups, database migrations and a staging environment.
- Add error monitoring and basic tests around sign-up, payments and other critical flows.
- Fix performance hotspots: pagination, indexes and caching.
- Refactor duplicated logic into shared, documented modules.
Keep the speed you gained
The goal is not to slow a startup down. A well-set-up codebase lets you keep using AI tools productively — with tests and reviews catching mistakes before your customers do. Many of our clients keep prototyping new ideas with AI builders and bring the ones that work into the production codebase.
The bottom line
Vibe coding is a superb way to find out whether an idea deserves to exist. Once it does, treat the prototype as a detailed specification, not a finished product. A few weeks of focused engineering can turn it into something you can confidently sell, scale and raise money on.