Vibe Code Audit & App Rescue
You built the prototype. Now let an engineer take it from here.
Built with Lovable, Base44, Bolt, Replit, Cursor, Claude Code, v0, Windsurf, or another AI coding tool? We review what really works, what is fragile, what should stay, and what must change before you trust the product with real users.
Built with one of these, or something similar?
From Prototype to Production
AI coding tools can get a product surprisingly far. The handoff point arrives when the app needs real users, permissions, payments, private data, integrations, reliability, or an engineering team that has to understand and extend the system.
We do not grade your project by how “AI-generated” it looks. We trace the product, inspect the engineering, and tell you what you actually have.
Feature Reality Check
What We Check
A useful audit has to go beyond style warnings. We review product behavior and the engineering underneath it, including the areas that become expensive when they fail in production.
We trace the important user journeys and separate working features from partial, mocked, happy-path-only, or disconnected behavior.
We look at whether another engineer can understand the system and whether the current structure can survive the next round of features.
We inspect the areas that become critical once real users, private data, payments, and external services enter the product.
We identify launch blockers and operational gaps that usually stay hidden while a founder is testing the happy path.
Audit Deliverables
A plain-English map of what is working, partially implemented, broken, mocked, or missing.
A decision for each important area so useful work is preserved and unnecessary rewrites are avoided.
The issues that should be addressed before you trust the product with users, money, or sensitive data.
Critical first, important next, later after launch. No unranked wall of technical findings.
A practical route into focused fixes, a rescue sprint, production hardening, or continued engineering.
Engineering Decision
We do not start from the assumption that AI-generated code should be thrown away. Each important part of the app gets a practical engineering decision.
Sound parts that already do their job and do not need rewriting just because AI helped create them.
Useful parts that should stay but need targeted engineering before you rely on them in production.
Only the pieces where rebuilding is clearly safer, simpler, or cheaper than continuing to patch them.
AI Builder Reviews
Different tools leave different failure modes behind. We adapt the review to the stack actually running your app.
We pay close attention to authentication, RLS policies, database permissions, edge functions, storage access, webhooks, and whether the UI really matches the backend rules.
We review application behavior, integrations, backend dependencies, data ownership, maintainability, export/handoff constraints, and the safest path if you want engineers to take over.
We trace frontend/backend connections, environment configuration, databases, third-party APIs, deployments, payments, and the places where a generated prototype can fail outside the demo flow.
We look for architecture drift, duplicated logic, unsafe assumptions, dependency problems, missing tests, inconsistent patterns, and code that became difficult to reason about after many AI-assisted iterations.
From Audit to Rescue
App or preview link, repository access when available, AI tools used, and the areas you are least confident about.
We inspect behavior and code together so the review reflects the product users actually experience.
Architecture, auth, data rules, integrations, payments, deployment, reliability, maintainability, and cost.
You get the Keep / Fix / Replace map, launch blockers, and a practical engineering sequence.
Use the report with your team, or have XSOLAI implement the fixes and continue engineering the product.
Good Fit
What to Send
Do not send passwords, private API keys, production secrets, or credentials by email or chat. We can arrange secure access when it is needed.
Scope & Trust
This is an engineering and production-readiness review. We can identify security engineering risks in the product, but this is not a formal penetration test, SOC 2 audit, PCI assessment, or compliance certification.
Questions
A vibe code audit is an engineering review of an app built largely with AI coding tools. We assess whether the product really works end-to-end, how maintainable the code and architecture are, whether authentication and data rules are safe, and what needs to change before production.
No. The goal is to preserve useful work. We recommend replacing a component only when there is a clear engineering reason to do so.
Yes. The review is based on the resulting application, codebase, data flows, integrations, and production risks rather than one specific AI builder.
Yes. Where those systems are part of the app, we can review authentication and authorization, Supabase permissions and RLS, payment and webhook flows, secrets, API validation, and related production risks.
No. This is an engineering and production-readiness review. We can identify security engineering risks in the product, but it is not a formal penetration test, SOC 2 audit, PCI assessment, or compliance certification.
That is a strong reason to review the highest-risk production flows first, especially authentication, permissions, payments, data handling, backups, failure states, and deployment.
Yes. The audit can lead into a focused fix, rescue sprint, production hardening, or ongoing engineering work if that is the right next step.
Start with the app URL, repository access when available, the AI tools you used, whether the app has users or payments, and the areas you are worried about. Never send passwords, API keys, or production secrets by email or chat.
Your prototype already has value
Send us the app and the part you are worried about. We will start with the engineering reality, then decide whether the right next move is a focused fix, rescue sprint, production hardening, or continued development.