Your code is never stored. Processing exclusively in EU data centers. GDPR-compliant.
View our security standardsAI Code Review
Automated AI code reviews with 4-phase analysis. HTML and JSON reports, inline PR comments, CI integration. GDPR-compliant, EU-hosted.
Multi-Perspective Review
Architecture, security, and performance analysis in one structured review.
HTML & JSON Reports
Rich reports for sharing, archiving, and tracking.
CI Integration
`code-review-ci` command for automated pipelines.
Staged & Full Review
Review staged changes or the entire codebase.
Diff-Base Review
Compare against any branch or commit.
Pre-Checks
Automated lint and type-check before the review runs.
The 4-Phase Review Workflow
Every code review runs through four sequential phases. The result is a structured report with actionable findings — not just a list of style complaints.
Discovery
Identify which files changed (git diff, staged, or full project). Build a dependency map to understand the blast radius of each change.
Analysis
Multi-perspective deep dive: code quality, architecture alignment, performance anti-patterns, security issues, test coverage gaps, and documentation completeness.
Verification
Each finding is cross-referenced against the full codebase. False positives are removed. Remaining findings are ranked by severity and impact.
Suggestions
For every confirmed finding, the reviewer generates a concrete improvement suggestion — with a code example where applicable.
Output Formats
Choose how you want to consume review results — from a visual HTML dashboard to machine-readable JSON for custom tooling.
HTML Report
A self-contained visual dashboard with findings grouped by severity, file, and category. Share it with your team or archive it for audits.
JSON Report
Structured findings with file path, line number, severity, category, and suggestion. Ideal for custom dashboards or integrating with your own tooling.
PR Comments
Findings posted as inline review comments on the exact lines in GitHub or GitLab. The AI can also submit an overall verdict: APPROVE or REQUEST_CHANGES.
Terminal Text
Human-readable summary streamed to stdout. Perfect for the interactive /review chat command.
CI/CD Integration
Integrate AI reviews into your pull request workflow with two lines of GitHub Actions config.
- name: AI Code Review
run: lurus code-review-ci --pr-comments --verdict --fail-on high
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} Verdicts
APPROVE No findings at or above the configured severity — PR is automatically approved. REQUEST_CHANGES Blocking findings found — PR review is submitted with required changes. COMMENT Findings below threshold — posted as comments without a blocking verdict. Review Scope Options
| Flag | Scope | Best For |
|---|---|---|
--diff (default) | Changed files since last commit | Pull requests and feature branches |
--staged | Only staged (git add) changes | Pre-commit hook integration |
--full | Entire project | Initial audit or quarterly review |
--diff-base main | Comparison against a branch or ref | Long-running feature branches |
GDPR & Source Code Privacy
Your source code is processed exclusively on EU-hosted servers and discarded immediately after the review. This makes Lurus Code suitable for regulated industries where code confidentiality is a contractual requirement.
- Code processed on EU servers only (Germany, France)
- Zero code retention — discarded after review completes
- DPA (Art. 28 GDPR) available for paid plans
- Suitable for fintech, medtech, and government software teams
Frequently Asked Questions
What is AI code review?
How does AI code review compare to manual peer review?
Does Lurus Code review my code on EU servers?
What output formats does the code review support?
Can I integrate AI code review into my CI/CD pipeline?
Automate code quality
Integrate AI reviews into your workflow, from the IDE to your CI pipeline.
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