AI CODE REVIEW
AI code review that does not guess
AI coding tools can generate working software fast. VCX reviews the output with deterministic analyzers so you get repeatable findings, exact files, line numbers, and proof before you ship.
For developers using Cursor, Copilot, Claude Code, bolt.new, Lovable, or any AI-assisted workflow.
Rule-based checks for SQL injection, XSS, hardcoded secrets, insecure auth patterns, and dependency risk.
Repeatable results: the same code produces the same findings, instead of probabilistic AI review drift.
Plain-English explanations plus file-level evidence for founders and developers who need to understand what shipped.
USE CASES
Where ai code review helps
Use VCX when AI helped create the code and you need verifiable security, architecture, and maintainability evidence before production launch.
Pre-launch AI code audit
Scan the whole repository before users touch it, money flows through it, or production depends on unreviewed generated code.
Security review for generated code
Catch high-risk patterns AI assistants commonly introduce, including injection, exposed secrets, and missing authorization checks.
Architecture understanding
Map modules and connections so you can understand the app your prompts assembled before the structure hardens.
FAQ
Questions teams ask before trusting an AI-generated codebase
Is VCX another AI code reviewer?
No. VCX uses deterministic static analysis for core findings. Optional AI Fix Suggestions can explain fixes, but the scan itself is built around repeatable rules and evidence.
Why not let AI review AI-generated code?
Because language models can miss concrete vulnerabilities or overstate uncertain conclusions. VCX is built to find verifiable patterns first, then explain them clearly.
What code does VCX work best for?
VCX is strongest on web apps and AI-generated SaaS projects using TypeScript, JavaScript, Python, and common package ecosystems.
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