Support for Apps Built by LLMs: Fix, Maintain and Scale AI-Generated Code

Signs your AI-built app needs a human engineer

• The AI keeps “fixing” the same bug, or every fix breaks something else.
• Nobody can explain how the app is structured; files are huge and duplicated.
• It works on your laptop but fails when deployed.
• It is slow, or the AI bill is climbing.
• Users report data problems, login issues or payment errors.
• You are about to raise money, sign a customer or pass a security questionnaire.

A rescue process that works

1. Triage (1 day). Read the code, run it, list the top risks and rank them by business impact.
2. Stabilise. Pin dependencies, add the missing environment configuration, get a reproducible build and a working staging environment.
3. Add safety nets. Smoke tests around the critical flows (signup, checkout, core feature) so changes stop breaking things silently.
4. Fix security and data issues first. Secrets, authorisation, database rules, backups.
5. Refactor the worst parts. Split mega-files, remove dead code, give each module one job.
6. Automate deployment and add monitoring so problems are visible before users complain.
7. Hand over or maintain. Either a documented handover to your team or a monthly support plan.

What ongoing support covers

Which tools do we support?

Apps generated or assisted by Claude and Claude Code, ChatGPT and Codex, Cursor, GitHub Copilot, Windsurf, Lovable, Bolt, v0, Replit, Base44 and Gemini. Stacks include React/Next.js, Node, Python (FastAPI, Django, Flask), Flutter, React Native, Supabase, Firebase and PostgreSQL.

Why not just keep prompting the AI?

AI assistants are strong at local changes and weak at holding the whole system in mind. Past a certain size, prompting alone causes regressions. A human engineer restores structure so that the AI tools become productive again, which is why we use AI tools ourselves and keep a human accountable for the result.

Why AI coding tools get stuck

Understanding the failure modes makes it easier to fix them. AI coding tools work within a limited context window, so as a project grows they lose track of earlier decisions, duplicate logic instead of reusing it, and “fix” symptoms in one file while breaking an assumption in another. They also optimise for code that runs, not code that is maintainable, so a project can reach 10,000 lines with no tests, no consistent patterns and several half-finished approaches living side by side.

• Loop of regressions: each fix introduces a new bug because nothing tests the old behaviour.
• Hidden coupling: state and data shapes are shared implicitly across files.
• Hallucinated APIs: code calls library functions that do not exist or are outdated.
• Dependency drift: unpinned packages update and break the build.
• Environment gaps: works locally because of a local file, port or variable that production lacks.

Three typical rescue scenarios

What you should ask any developer who takes over

• Will I own the repository, hosting and database accounts?
• How will you prevent new changes from breaking existing features?
• What will you document?
• How do you handle secrets and customer data?
• What is the fixed scope and price for the first milestone?

Frequently asked questions

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