Vibe Coding Cleanup: Make AI-Built Code Safe to Ship

Sometimes, and the audit answers it honestly. The deciding factors are usually whether the data model is sound and whether authorisation can be centralised, if both are salvageable, incremental refactoring is almost always cheaper and far less risky, because you keep shipping. If they are not, we will say replace, and scope it in slices rather than as a big-bang rewrite.

Less than the current state does. The first two weeks are audit and tests, which produces no features, after that, cleanup runs alongside feature work in the same sprints. Most teams find velocity improves within a month or two, because changes stop causing unrelated breakages.

We start with a short discovery call to clarify goals, constraints, and success metrics. Then we identify high-impact AI use cases, review your data and systems, and propose a phased roadmap with costs and risks attached to each phase.

Discovery runs 2 to 4 weeks, an MVP typically lands in 8 to 16 weeks of weekly-demo sprints, and operate is an ongoing arrangement with monitoring and support after launch.

Fixed-scope work is priced per milestone, dedicated teams are billed monthly per seat, and staff augmentation is weekly or monthly. You pick the model that fits and can switch between phases. There is a fuller breakdown of what moves the number on our AI development cost page.

You do. The full repository, documentation, and deployment transfer to you at every milestone, with no lock-in to us.

NDAs from day one, scoped access, and data residency decided at the architecture stage. We work within HIPAA, SOC 2, and similar requirements where they apply.

We monitor accuracy, cost, and uptime, run regression checks as models drift, and keep a defined escalation path with support so the system stays healthy after release.