Strength 03 · Code Risk & Launch Readiness
Vibe-Code & AI-Built MVP Launch Review.
A technical review for AI-built MVPs before customers, investors, or enterprise buyers rely on them. The check covers 10 risk areas, 4 severity levels, and the judgement that comes from production software work since 2016.
Audit, repair, secure, test and productionize AI-generated codebases. For founders who shipped fast with Cursor, Lovable, Bolt, Replit, Claude or Codex and now need to know what breaks before real users or an investor's technical diligence finds it.
The Review
What The Review Actually Checks.
AI-generated code often looks clean while hiding risk in authentication, permissions, payments, data integrity, secrets, and concurrent usage. The review starts where those failures usually appear.
Security & Access
Auth flows, exposed secrets, injection surfaces, permission checks and endpoints that are public when nobody meant them to be.
Data Integrity
Schema, migrations, transactions, race conditions and what happens when a write fails halfway through.
Architecture & Debt
Coupling, duplicated logic, dead code, and which modules are cheaper to rewrite than to keep repairing.
Production Readiness
Tests, error handling, logging, environments, deployment and cost exposure at realistic load.
The risk pass focuses on 10 launch-risk areas: authentication, authorization, secrets, database writes, payment logic, webhooks, API boundaries, deployment, tests, and maintainability.
The launch review covers 10 risk areas, 4 severity levels, and 3 output layers, with production judgement from software work since 2016 across 80+ shipped applications.
The Shift
From Hoping It Holds To Knowing It Does.
These four situations account for most of the founders who book this check, and what they walk away with.
What You Get
What Lands In Your Inbox.
The review can stand alone as a technical risk report. If critical issues are found, the repair sprint focuses only on the problems that affect launch, security, data, or diligence.
Security Audit
Authentication, authorization, secret handling, injection surfaces and publicly reachable endpoints.
Data & Integrity assessment
Schema design, migrations, transactional safety and concurrency behaviour under real usage.
Repair & Hardening
Critical findings fixed in place, with the reasoning documented so your team learns the pattern.
Test & CI Baseline
A working test suite and deployment pipeline so future changes are verifiable instead of hopeful.
Repair Or Rewrite Verdict
Module-by-module judgement on what to keep, what to refactor and what to throw away now rather than later.
Diligence Pack
Architecture diagrams and written answers ready for investor or enterprise technical due diligence.
A founder launched an AI-built marketplace MVP to early users. The review found exposed admin routes, an unguarded payment webhook and no transactional integrity on orders. Critical issues were repaired and a test baseline was in place before the enterprise pilot began.
Engagement
Ten Working Days, End To End.
The optional repair sprint starts only after you choose which risks should be fixed first.
Intake
Repository access, environment walkthrough and what the product is meant to do.
Deep Technical Review
Security, data, architecture and production-readiness inspection with findings logged.
Report
Prioritised written risk report with severity, impact and remediation cost.
Repair Sprint
Critical fixes applied, test baseline established, deployment hardened.
Straight Answers
What Founders Want To Know.
Which AI coding tools do you risk pass code from?
Cursor, Lovable, Bolt, Replit, Claude, Codex, Copilot and anything else that produced the repository. The tool matters less than the failure patterns, which repeat across all of them.
Will you tell me to rewrite everything?
Rarely. Most AI-built MVPs have a solid, salvageable core and a handful of dangerous modules. The report says which is which, per module, with the cost of each path attached.
Do you fix the issues or only report them?
Either. The audit is a standalone engagement ending in a written report. The repair sprint is optional, and most founders take it for the critical findings.
Is this useful before investor due diligence?
That is one of the most common reasons founders book it. You want the problems found by someone on your side, with time to fix them, rather than by an investor's technical reviewer.
Find the Problems Before Your Users Do.
Give me repository access and a short walkthrough of what the product is meant to do. You get back a prioritised written risk report - severity, impact, and what each fix costs - reviewed personally, not by a junior with a review tool.
Direct founder-to-founder technical review, no account managers
Clear answer
Define a vibe-code MVP launch review
A vibe-code MVP launch review is a technical audit for products built quickly with tools like Cursor, Lovable, Bolt, Replit, Claude, Codex or Copilot. The technical pass checks whether the AI-generated code can survive real users, real data and technical diligence. It looks at authentication, permissions, data integrity, API behavior, deployment, logging, security, dependency risk, tests and failure cases. The point is not to shame fast prototyping. Fast building is useful. The risk starts when a prototype becomes a business system without anyone checking the parts the generator skipped, misunderstood or hardcoded.
Direct Answers
What This Page Answers.
Short, direct answers for founders, search engines, and AI crawlers checking this page.
Answer 01Define a vibe-code MVP launch risk pass
A vibe-code MVP launch launch check is a technical audit of an AI-built or AI-assisted codebase before real users, investors, or customers depend on it.
Answer 02Which AI-built codebases can be reviewed?
Codebases built with Cursor, Lovable, Bolt, Replit, Claude, Codex, Copilot, or a developer using AI tools can be reviewed. The tool matters less than the failure patterns in the repository.
Answer 03audit checklist
The check checks authentication, authorization, payment logic, secrets, database integrity, deployment risk, API handling, test coverage, maintainability, and the difference between demo behavior and production behavior.
Answer 04The result after critical issues are found?
Critical findings can be repaired in a focused sprint, with priority on security, data integrity, payment risk, deployment blockers, and minimum test coverage for launch.
Related Problems
Other Problems Founders Check Before Launch
Your AI-Built App Works. Is It Safe for Real Users?
Step-by-step self-check for auth, payments, data isolation, error handling and rollback before real users arrive.
Read the checklist →Customers Can Pay. But Access, Webhooks and Subscriptions May Not Work
The most common silent failure in AI-built SaaS. 7 things to check before the first real charge.
Check payments →Your AI Prototype Is Impressive. It Is Not Production-Ready Yet
What actually changes between a working demo and a system you can trust with real users and real data.
See what changes →