AI code review — quality checks for software your AI wrote.
AI code review for AI-generated software is a different problem from reviewing human-written code. Cursor, Copilot, and similar tools write code that compiles, passes your manual testing, and ships — but leaves behind hardcoded credentials, unchecked errors, zero tests, and dead code at a rate no human team would tolerate. inkode runs 20 automated checks against any repo and tells you what's hiding in the code your AI wrote.
The failure modes AI coding tools reliably introduce.
AI tools scaffold configuration with working values from training examples — which means real-looking API keys, hardcoded passwords, and database connection strings with credentials embedded. They work in development and ship to production.
When an AI tool isn't sure how a function should fail, it discards the error — x, _ := foo() in Go, bare try/except: pass in Python. The code runs. Errors disappear silently. Debugging becomes guesswork.
AI tools write application code on demand. They don't write tests unless you explicitly ask — and even when they do, the tests often assert only the happy path. Critical paths go untested. Regressions go undetected.
Every time you ask an AI tool to change an approach, the old approach often stays. Functions no one calls, variables no one reads, imports for packages no longer used. It adds up fast across dozens of AI-assisted sessions.
AI tools suggest package versions from their training cutoff — which may be a year or more out of date. The code works, the dependency tree is already vulnerable to published CVEs.
AI-generated functions tend to handle every edge case inline — deep nesting, long switch statements, high cyclomatic complexity. The function works. No one can change it without breaking something.
20 checks tuned for the post-AI codebase.
inkode detects which AI libraries your project uses and flags it in the scan header.
The AI anti-pattern no linter catches.
When you ask an AI tool to write a helper function, it writes one. When you ask again in a different file or a different session, it writes another one — slightly different, but doing the same thing. Over dozens of sessions you accumulate a cluster of semantically identical functions that textual diff tools won't flag because the variable names differ.
inkode uses an embedded Qwen2.5-Coder-0.5B model to extract function embeddings and pair them by cosine similarity. It finds the helpers your AI regenerated from scratch in a different package — and tells you which ones to consolidate.
Before you ship, before you raise, before you hire.
The hardcoded credentials and vulnerable dependencies in AI-scaffolded code are liabilities from the first deployment. Scan before you flip the switch — not after your first customer.
Investor technical due diligence always includes a security review. Walking in with your inkode score and a clean-up plan is a better conversation than waiting for their engineer to find the issues. See the pre-fundraise audit page.
Your first hire will read the codebase and form an opinion. Dead code, missing tests, and unhandled errors are the things senior engineers flag immediately. Get the scan done before they start.
Wire inkode into your CI pipeline. Every PR gets scanned. Quality gate fails if a secret or high-severity CVE is introduced. Regressions in the health score trigger an alert. The GitHub Action takes five minutes to set up.
Review your AI-generated code today
Install in one line. Scan in under a minute. Know what your AI wrote — and what it left behind.
See the per-language checks: all scanners for Go, Java, Python, Rust, and TypeScript.