Ship Code You Can Be Proud Of — Every Single Time
CodeChecks enforces four pillars of code quality — correctness, consistency, safety, and auditability — on every AI-assisted change, automatically.
Four pillars of code quality, all automated
Quality isn't one thing. CodeChecks checks four distinct dimensions on every run so nothing slips through.
Correctness
Code that does what the requirements say. The Verifier checks the diff against the stated task, not just against its own intuition.
Consistency
Code that matches your project's conventions. The Planner reads your repo before generating — import paths, naming, error handling — all consistent with your existing code.
Safety
Code that doesn't break your sensitive paths. Policy enforcement blocks changes to env configs, auth modules, and payment code unless you explicitly allow them.
Auditability
Code you can explain and reproduce. Every run produces an evidence bundle — repo SHA, patch hash, and verification results — so quality is provable, not assumed.
The AI code quality problems CodeChecks solves
AI coding tools are powerful. They also introduce new quality problems that didn't exist before.
The problem
AI tools generate plausible-looking but incorrect code
CodeChecks solution
CodeChecks verifies that the implementation actually matches the requirements — not just that it compiles.
The problem
Different engineers use AI tools differently, producing inconsistent output
CodeChecks solution
Org-wide policy rules and repo-aware generation mean every teammate's AI output follows the same conventions.
The problem
No audit trail for AI-assisted changes
CodeChecks solution
Evidence bundles capture the full context of every run: what was asked, what was generated, and whether it passed verification.
The problem
AI tools can touch sensitive files they shouldn't
CodeChecks solution
Policy flags lock env files, auth code, and payment logic by default. Requires explicit opt-in to change protected paths.
3
stages in every code quality check
2
AI models cross-validating every patch
0
unverified changes reach your repo
AI code quality — common questions
Is CodeChecks a linter replacement?
No — use both. Linters check style and syntax in milliseconds. CodeChecks checks correctness, policy compliance, and alignment with requirements — the things linters can't understand.
How does repo-aware generation improve code quality?
When the AI knows your existing conventions — module structure, import paths, error handling patterns — it generates code that matches them. You get output that looks like your team wrote it, not like a generic tutorial.
What does code quality look like with two AI models?
Claude generates the implementation. GPT-4o reviews it independently, returning a structured pass/fail verdict with reasoning. You see both the code and the verdict — not just the code.
Can I enforce team-specific quality rules?
Yes. Policy flags let you define which files and directories are protected. Combined with admin controls on the Gold Plan, you can set quality standards that apply to everyone's verification runs.
Start shipping higher-quality code today
Free plan. 10 task cycles per month. No credit card required.