Automated AI Code Review That Actually Catches Bugs
CodeChecks runs a three-stage review pipeline on every change — plan, implement, verify — so you ship reliable, policy-compliant code instead of guessing.
How the AI code review pipeline works
Three specialized AI agents — each with a single job — produce a result no single model can match.
Planner
Claude analyses your requirements and produces a structured task list with file plans and verification commands — so the AI knows exactly what to build before writing a single line.
Implementer
Claude generates a unified diff for each task. Dependency files, lockfiles, and sensitive files are protected by policy before the patch is delivered.
Verifier
GPT-4o cross-validates the implementation independently. Two models checking the same patch catches subtle logic errors neither would catch alone.
Why developers choose CodeChecks for AI code review
Most AI coding tools generate code and hand it back. CodeChecks generates, verifies, and explains — with an audit trail.
Repo-aware generation
CodeChecks reads your project structure, dependencies, and test framework before generating code — so every suggestion matches your conventions.
Evidence bundles
Every run produces a JSON record with repo SHA, patch hash, and step-by-step reproduce instructions. You have a full audit trail, not just a suggestion.
Policy enforcement
Sensitive files (env, auth, payment code) are locked by default. Changes to protected paths require explicit opt-in — reducing the blast radius of AI mistakes.
Multi-model cross-validation
Anthropic Claude plans and implements. OpenAI GPT-4o independently verifies. Disagreements between the two surface the highest-risk changes for human review.
Ship with confidence — not hope
3-stage
pipeline — plan, implement, verify
2 LLMs
cross-validating every patch
100%
evidence-backed audit trail
AI code review for every team size
Start free. Upgrade when you need more cycles.
AI code review — frequently asked questions
How is AI code review different from a linter?
Linters check syntax rules. AI code review understands intent — it catches logic bugs, architectural mismatches, and policy violations that static analysis misses.
Which languages does CodeChecks support?
Any language your LLM understands. In practice that means TypeScript, JavaScript, Python, Go, Rust, Java, C#, Ruby, and more — whatever your repo contains.
Does it replace human code review?
No — it augments it. CodeChecks catches the mechanical issues so your reviewers can focus on architecture, business logic, and team-specific context.
How long does a review take?
A typical task cycle completes in 20–60 seconds. Larger diffs take longer, but you always see streaming progress as the pipeline runs.
Start your first AI code review in minutes
Free plan. No credit card required. 10 task cycles per month.
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