Code quality for the post-AI era

AI changed how code gets written. Cursor, Copilot, and ChatGPT let a single founder build what used to take a team. Velocity went through the roof. But nobody changed how code gets reviewed.

The result: codebases that compile and ship but carry hidden risks — hardcoded secrets, zero test coverage, vulnerable dependencies, tightly coupled modules that break in cascade. The developers who wrote the code don't know what they don't know, and the AI that generated it doesn't care.

inkode exists to close that gap. We combine automated static analysis with expert human review to give founders and engineering teams a clear picture of what's actually in their code — and a prioritized plan to fix it.

How we think about code quality

Automation first, humans where it matters

Machines are better at scanning every file for secrets and CVEs. Humans are better at understanding architectural risk and prioritizing remediation. We use both.

No source code leaves your machine

The CLI runs locally. Only file paths, git metadata, and code metrics are uploaded. Your intellectual property stays yours. We analyse the shape of the code, not the code itself.

Best-effort over gatekeeping

If a check fails or a tool is missing, the scan still runs. Skipped checks redistribute their weight. A report is always produced. We never block your workflow.

Scores are starting points, not verdicts

A score of 38 doesn't mean your code is bad. It means there are specific, fixable issues. Every finding maps to a concrete file, line, and action. The score goes up as you fix them.

20 checks across 5 risk categories

Each check targets a specific dimension of code health that AI-generated code tends to get wrong. Coverage spans Go, Python, JavaScript, TypeScript, Java, and Rust.

Secrets, dependency CVEs (Go, JS, Python, Rust, Java), test coverage, cyclomatic complexity, copy-paste & semantic duplication, change coupling, circular imports, dead code, infrastructure misconfigs — and ten more.

See every check, in detail →
Scoring

Each category has a weight in the overall score. When checks are skipped, their weight redistributes proportionally so the score stays meaningful.

30%
Security
20%
Testing
20%
Maintainability
15%
Complexity
15%
Change Risk

Questions about your codebase?

Whether you need a one-time audit or ongoing quality monitoring, we'd love to hear about your project.

hello@inkode.co