Review code with production context.
CloudThinker connects every pull request to runtime signals, incidents, and your engineering conventions — finding the issues generic reviewers miss and proposing fixes your team can trust.
Trusted by cloud teams and ecosystem partners
How it works
Every PR, reviewed
Auto modeThe diff, the linked issue, and the services the change touches.
Runtime signals, incident history, and your team conventions.
Line-level findings with the fix, ranked by blast radius.
Re-checks the push, closes what is fixed, keeps what is not.
Capabilities
Why teams choose CloudThinker Review
Security First
Automatically detect security vulnerabilities and potential exploits before they reach production.
Pipeline Error Investigation
Get instant feedback on your pull requests. Our AI reviews code in seconds, not hours.
Precision Accuracy
Machine learning models trained on millions of code reviews ensure accurate, relevant feedback.
Team Collaboration
Foster knowledge sharing and maintain consistent code standards across your entire team.
Seamless Integration
Works with GitHub, GitLab, and more. Set up in minutes with zero configuration.
Continuous Learning
Our AI adapts to your team's coding style and preferences over time for personalized reviews.
Performance
Proven results with real data
Bug detection
0%
Critical & high-severity bugs caught
Review time
0 min
Average from commit to feedback
Precision
0%
True positive rate
Security coverage
0%
OWASP Top 10 vulnerabilities
Proven in real cloud operations
Put AI code review on live merge requests at roughly 97% precision, catching every defect observable in the code before human review or QC.
Built three-region infrastructure and automated SOC 2, HIPAA, and GDPR controls in four weeks — reducing operations workload by 80% while maintaining 99.9% uptime.
Automated 80% of daily operations and reduced AWS spend by 30%, with continuous, auditable controls.
Applied CloudThinker across multi-cloud and Kubernetes, reaching 98.4% bug detection on AI-assisted pull requests and reducing incident noise by 95%.
Hardened AWS infrastructure and shortened incident investigation and remediation from hours to under 30 minutes.
Added AI code review before quality control, 24/7 managed cloud operations, and automated health, cost, and performance reporting — preserving delivery speed while improving quality.
Runs its Azure cloud, application platform, and IoT fleet with AI code review, cost optimization, and automated daily health checks.
Reduced RDS replica costs by 50% within three months while strengthening infrastructure security.
Benchmark
Independent benchmark on 37 real-world bugs
Five AI code review tools tested across four open-source repositories.
Test dataset
Case library
Detailed comparison results
Case Library shows how each tool performed across PRs, with tables listing bug summaries, severities, and whether tools caught the issue based on explicit line-level findings.
| PR title / bug description | CloudThinker | Greptile | Copilot | CodeRabbit | Cursor | Graphite |
|---|---|---|---|---|---|---|
Optimize spans buffer insertion with eviction during insert Negative offset cursor manipulation bypasses pagination boundaries — Security vulnerability | ||||||
Support upsampled error count with performance optimizations sample_rate = 0.0 is falsy and skipped — Affects test utilities only | ||||||
GitHub OAuth Security Enhancement Null reference if github_authenticated_user state is missing — Crashes in production | ||||||
Replays Self-Serve Bulk Delete System Breaking changes in error response format — Breaks existing API consumers | ||||||
Span Buffer Multiprocess Enhancement with Health Monitoring Inconsistent metric tagging with 'shard' and 'shards' — Hinders monitoring/debugging | ||||||
Implement cross-system issue synchronization Shared mutable default in dataclass timestamp — Unexpected shared state | ||||||
Reorganize incident creation / issue occurrence logic Using stale config variable instead of updated one — Uses stale configuration | ||||||
Add hook for producing occurrences from the stateful detector Incomplete implementation (only contains pass) — Missing core logic | ||||||
| Total catches | 7/8 | 6/8 | 3/8 | 3/8 | 2/8 | 0/8 |
A subset of datasets from the original Greptile benchmark was excluded due to insufficient ground truth reliability.
Start with AWS support and a CloudThinker FDE.See ROI on day one.
Your AWS agreement and credits carry straight over, and a CloudThinker forward deployed engineer does the onboarding, so the first result lands on day one.
Up to $200K in AWS credits
Applied to your own AWS account.
AWS AI Services Competency
Validated for Agentic AI Consulting.
Covered 24/7, on your approval
Under HIPAA, GDPR and SOC 2 controls.