Process and delivery

How to take a vibe-coded prototype to production

A prototype from Lovable, Cursor, Bolt, or Replit can help you validate the product. Before inviting real users, identify what code can be maintained, what controls have been verified and what must change for a safe pilot.

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Short answer

Keep the validated workflow and any maintainable code, then release only after you can demonstrate access control, data integrity, repeatable builds and recovery.

Best for

  • Founders who built a prototype in an AI tool and now have real interest
  • Teams that validated an idea fast and need it to hold up for users
  • Products heading into a pilot, a launch, or an enterprise conversation
  • Anyone unsure what in their AI-generated code is safe to keep

Not for

  • Prototypes that have not yet proven anyone wants the product
  • Throwaway internal demos with no path to real users
  • Founders who want to ship the raw prototype to production untouched
// 01

Compare the options

For each area, compare evidence from the demo with the checks required for the pilot.

Concern Demo evidence Release evidence
Auth and access

A single demonstration login works.

Ordinary user roles and session behavior verified.

Data integrity

Sample data displays as expected.

Validation, consistency and a tested restore.

Security

No findings were observed during the demo.

Credential handling, dependency review and input validation checked.

Under load

One demonstration completed successfully.

Concurrent use tested with representative workloads.

Cost if it breaks

Recovery cost and data impact are unknown.

Recovery owner, rehearsal results and accepted limits recorded.

Speed to real launch

A working demo is available.

Estimate the gaps from a reviewed release scope.

// 02

Where Wavect lands on this

Decide what to keep, repair or replace

Start with the user journey that earned the pilot. List which parts can be built from a clean checkout, which depend on an export or vendor account and which use demo data or placeholder logic. Keep components that pass the agreed checks and have a maintainer. Repair local gaps. Replace components when they cannot meet the required controls or be operated by the team that will own them. Compare the options in shipping the prototype or hardening it first.

Lovable documents built-in scans for code, database access and dependencies. Use available reports to plan the review and verify the release controls against the actual deployment.

Require a release you can reproduce

Move the candidate into a client-owned repository and separate production accounts from the prototype. Record the dependency lockfile and build artifact, and verify that the artifact tested is the one being released. If a demo credential was shared or committed, revoke or rotate it. Test with two ordinary users in different organizations and confirm that each can access only its own records. Rehearse a backup restore and verify the resulting records and permissions. The NIST Secure Software Development Framework covers practices for securing the development and release process. SLSA explains how to verify an artifact against its build provenance.

Open the pilot to an agreed cohort with an owner who can halt it. Track failures in the critical journey and agree how to recover writes already made before expanding access. Twinsoft AI reached enterprise pilot readiness in two weeks in its own engagement. PromptID was a separate build that reached production in six weeks. Use the case studies to see the scope each team delivered.

// 03

Cost, risk and timeline

Cost Scope the prototype reviewAgree the review scope from the repository, hosting accounts, data and pilot needs. Confirm the fee and which repairs it covers separately.
Risk Shipping foundations that fake itFaked auth and missing data integrity look fine in a demo and cause breaches or data loss in production.
Timeline Twinsoft AI case: 2 weeksTwinsoft AI went from vibe-coded prototype to enterprise pilot-ready in two weeks.
// 04

Where this usually goes wrong

  • Shipping faked or client-side auth that anyone can bypass.
  • No data validation or constraints, so the database quietly corrupts over time.
  • Secrets and API keys hardcoded in the prototype and pushed to production.
  • Zero automated tests, so every fix risks breaking something else.
  • Built for one user, so it falls over the first time real traffic arrives.
  • Rewriting from scratch out of fear, and throwing away the validated head start.
// 05

The checklist

Before a vibe-coded prototype goes anywhere near real users, work through these.

Tick items off as you go. Nothing is stored or sent.

// 06

What this looks like in our work

We turn AI-generated prototypes into products that survive real users, without throwing away the work that proved the idea.

These are selected projects, not our full portfolio. We have shipped 100+ products since 2018.

// 07

When this fits, and when it does not

// 01

When Wavect is the right fit

  • You built a prototype in an AI tool and now have real interest to serve.
  • You want to keep the validated work and harden the foundations under it.
  • You are heading into a pilot, a launch, or an enterprise conversation.
  • You want experienced engineers to decide what carries over and what does not.
// 02

When we are not the fit

  • You have not yet proven anyone wants the product, so harden nothing yet.
  • You want to ship the raw prototype to production untouched.
  • It is an internal throwaway with no path to real users.
  • You expect production-grade work at prototype-tool prices.

If you have a prototype with traction and need it production-ready, a short Discovery phase tells you what to keep and what to rebuild.

// 09

FAQs

Release once you have evidence for the critical user journey, real account permissions, data integrity, a repeatable build and recovery. The product and the pilot scope determine which checks are required.
Start by testing the existing implementation against the release criteria. Keep the validated flows and maintainable components, repair gaps with a clear fix and replace components that cannot meet the operating requirements.
The workflow, user feedback and useful components can carry over. Verify access controls, data handling, dependency support, deployment and recovery independently. The results tell you which parts need repair or replacement.
Estimate it after reviewing the code, data and planned pilot. Twinsoft AI reached enterprise pilot readiness in two weeks. PromptID reached production in six weeks in a separate project. Use those scopes to understand the examples and estimate your own work.
A prototype is useful for learning whether users can complete the intended task and find it valuable. Set separate acceptance criteria for a pilot that handles real accounts or data.
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