Manage what AI builds.
Govern what reaches users.

CloudAEye reads your whole codebase, validates the task, scores AI slop, catches OWASP and MCP risk, writes the tests and docs, explains what broke, and gates the merge. Ship production-quality code 4× faster with one platform, from your terminal to production.

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$50 free credits per month. No credit card.

Live cloudaeye pre-commit signal engine · 11 verification checkpoints Interactive: click any checkpoint Drag sideways to explore →

Recognized by industry leaders in AI & developer productivity

TechCrunch Battlefield 200 NVIDIA Inception Wellfound Top 10 AI Startup Grind Global

Independent open-source evaluation, 2026

A reviewer is only useful if it is right.

In an independent open-source benchmark of AI code review tools, CloudAEye ranked #1 with a 66.7% F1 score, outperforming Qodo Extended, Cursor Bugbot, Greptile, Claude Code, GitHub Copilot, and CodeRabbit.

F1 score (higher is better) 0 to 70
CloudAEye
66.7
Cubic
61.8
Qodo
57.9
Cursor
45.5
Greptile
44.0
Claude Code
37.6
Copilot
37.0
CodeRabbit
35.2

Relative F1 improvement

vs Claude Code
+77.4%
vs Cursor
+46.6%
vs GitHub Copilot
+80.3%
vs CodeRabbit
+89.5%

Two ways in, one decision

It runs before the commit. And again on the PR.

Every capability (codebase queries, task validation, slop scoring, security, test generation, the merge gate) runs in both places, sharing the same findings, the same rules and the same approval record.

Pre-commit

In your terminal, before a branch exists

Review the uncommitted worktree while the coding agent still holds the context that produced it, so the fix costs one prompt.

  • cloudaeye:review: bugs and security in one pass
  • cloudaeye:security: OWASP, LLM, agentic, MCP
  • cloudaeye:check-task: validate against the ticket
  • cloudaeye:ask: query the codebase and blast radius
Claude CodeVS CodeCursorCLIMCP server
Post-commit Native integration

On the pull request, as a required gate

A GitHub App, GitLab and Bitbucket integration that reviews every pull request automatically and can block the merge when a selected checklist item is set to Error and required checks are enabled.

  • Automated review on every PR, with inline comments
  • PR Checklist: 75+ checks, enforced as a status check
  • @cloudaeye /review and /describe on demand
  • CI hooks for GitHub Actions, GitLab CI, Bitbucket Pipelines and Jenkins
GitHub AppGitLabBitbucketJira

Evidence, not opinions

Every finding arrives with a location, a severity and a fix.

Rules · feedback · learning

Deterministic, enforceable standards.

Write rules in plain English. Leave feedback on any comment in an existing pull request. CloudAEye turns that into learning: a new rule applied automatically to every review that follows.

Rules 3 active

Flag any hardcoded API keys, passwords or secret tokens.

Security *all repositories

Every API handler must log the request id before returning.

Team standards src/api/path scoped

Catch specific exceptions, never a bare except.

Code quality .pyfile pattern
No DSL, no YAML. Describe the standard the way you would say it in review.
Feedback · on an existing pull request
PR #1094 checkout/retry.ts:12
Comment is not true “We allow bare except in the migration scripts on purpose.”
Learning · rule created automatically
Do not flag bare except under migrations. scripts/migrations/ · review, edit, enable or disable

Leave feedback on any open pull request in the console, or on GitHub, GitLab and Bitbucket. The rule it produces is context on every review after it.

  1. 01 Rules

    Your standards, in plain English

    Describe a standard in a sentence and choose its scope: every repository with *, a file pattern like .py, or a directory such as src/api/.

  2. 02 Feedback

    One thumbs-down on an existing PR

    Disagree with a comment on a pull request that is already open, pick a reason, add a line of context.

  3. 03 Learning

    It never raises that finding again

    CloudAEye writes the rule from the PRs you already reviewes and applies it as context to every later review.

  4. 04 PR Checklist

    75+ Built-in Checks

    Start with prebuilt checks across 9 categories. The PR Checklist runs as a status check on each pull request and can block the merge when a selected item is set to Error and required checks are enabled.

Everything in the box

One platform for the whole flood, not just the diff.

Review, security, task validation, codebase Q&A, test intelligence and generation in your terminal, your IDE, and your pull requests.

Review

findings
  • #1 F1 score of 21 tools66.7% on the independent 2026 benchmark
  • OWASP, LLM, agentic & MCP security/security
  • Task validation & approval/check_task · issue URL or Jira ID
  • Automated PR review/review
  • Bug hunting pass/inspect

Govern

merge gate
  • Intelligent PR review and risk-based approval/ultrareview
  • PR Checklist as a merge gate75+ prebuilt checks across 9 categories
  • Approval flow with feedback loopVerdicts train the next review
  • AI slop score/slop_score · Detect low-quality AI-generated code
  • Risk score/risk_score · Identify and prioritize high-risk PRs
  • Custom rules & codebase context
  • Create tracking issues/create_issue
  • Triage/cloudaeye:triage-prs · Prioritize PRs by business impact
  • Assign Reviewer/assign_reviewer · Identify the best reviewer for a PR

Understand

context
  • Deep, interconnected understanding of the entire codebase
  • Tech stack detection
  • Describe PR & commits/describe
  • Detect architectural violations and dependency issues/review_arch
  • Visualize and understand execution flow changes/control_flow
  • Understand PR changes at a glance/pr_walkthrough
  • Explains a Jira or GitHub issue/explain
  • Q&A on codebase & documents/ask
  • Dashboard, team insights & analytics
  • Unit tests/test
  • Docstrings & inline docs/add_docs
  • Fix plans grounded in the code graph/implement

Test intelligence

Test RCA
  • Test RCA for failed tests
  • Flaky test detection
  • Test triaging
  • GitHub Actions & Jenkins

Explore Test RCA →

Where it runs

surface
  • Pre-commit, in your terminalClaude Code, Codex & Cursor plugins
  • Native GitHub App, GitLab & BitbucketAutomated review + checklist gate on every pull request
  • Jira
  • Self-hosted on AWSTerraform bundle onto Amazon ECS in your own account

What it costs

free tier
  • 50 free credits per month on the free plan, $1/credit after that.
  • Bring your own LLM keys

See pricing →

Deployment trust

Your code. Your infrastructure. Your decision.

Zero-day retention

Never stored, never trained on

Diffs are analyzed ephemerally in memory and discarded the moment the review completes.

Self-hosted

Runs in your own AWS account

A Terraform bundle deploys CloudAEye onto Amazon ECS inside your VPC, so code never leaves your VPC.

Governance

SSO, RBAC and audit logs

OAuth, SAML 2.0, OIDC

Works where you already work

Seamless integration with your workflow.

Writing

Latest from our blog.

How we think about reviewing AI-generated code, securing the modern AI stack, and running CloudAEye inside your own perimeter.

Self-hosting

July 17, 2026 · 10 min read

Deploy CloudAEye Code Review On Premises

Fully self-hosted deployments let organizations adopt AI-powered code review without source code leaving their security perimeter.

Read the post

Security

May 5, 2026 · 15 min read

A Developer’s Tour of Securing the Modern AI Stack

LLMs, agents and MCP layers break traditional security assumptions and quietly introduce a surface the OWASP Top 10 never anticipated.

Read the post

PR Checklist

February 24, 2026 · 12 min read

Building a World-Class Engineering Culture with PR Checklists

Engineering excellence at scale needs policy-driven discipline, not heroics. How checklists turn review standards into something enforceable.

Read the post

All posts

Out in the world

Events & webinars.

Where the CloudAEye team has been talking about shipping AI-generated code safely on stage, on panels, and on air.

Panel: Efficiently Develop and Scale AI Applications
Panel

Efficiently Develop and Scale AI Applications

Nazrul Islam joins Race Capital, Comet, Malbek, Cloudidr and Qlay on taking AI applications from prototype to production scale.

With DGUS · Qlay · Cloudidr

Mini Conference: AI in Production
Mini conference

AI in Production

A working session on what actually breaks when AI systems meet production traffic, alongside AMD, Redis, Guild.ai and Cracking Gen|AI.

Cracking Gen|AI · AMD · Redis

Conference

TechCrunch Disrupt: Startup Battlefield 200

Selected for the Battlefield 200 cohort at TechCrunch Disrupt in San Francisco.

San Francisco · 19–21 September

Collision, Toronto
Conference

Collision, Toronto

Showing CloudAEye to one of the largest gatherings of engineering and startup teams in North America.

Toronto · 17–20 June 2024

Common questions

Everything you need to know.

No. Code review is just one of several capabilities CloudAEye offers. It is an AI-powered engineering workflow platform that helps teams manage AI-generated code end to end.

Coding agents are flooding engineering pipelines with massive PRs faster than teams can validate, prioritize, understand, and secure them. CloudAEye Review Intelligence brings intelligent code review and governance directly into Claude Code, Codex, and Cursor workflows, helping teams ship with confidence.

Beyond code review, CloudAEye validates code changes against original tickets and requirements, answers questions across codebases and repository boundaries, generates unit tests and documentation, writes PR descriptions and commit messages, detects AI-generated code slop, assesses PR risk, performs Root Cause Analysis on failing and flaky tests, and enforces a 75+ item quality and security merge gate.

By automating key aspects of code review and agile development, CloudAEye helps developers catch issues earlier, reduce bottlenecks, improve engineering efficiency, and ship high-quality software with confidence.

Review is just the starting point. CloudAEye is the intelligent governance and automation layer for AI-driven software development.

CloudAEye scored a top 66.7% F1 in an independent 2026 benchmark of 21 tools, ahead of Claude Code (+77%), GitHub Copilot (+80%), Cursor (+47%) and CodeRabbit (+90%). Cross-referencing the whole codebase, git history and the issue spec is what keeps false positives down while still catching subtle logic bugs.

You run cloudaeye:review against your uncommitted worktree from Claude Code, Cursor, VS Code or the CLI. Findings come back in the same session, so the coding agent still has the full context and can fix them immediately, without a branch, a pull request, or a review round-trip.

No. CloudAEye handles the repetitive 80%: missing error paths, exposed secrets, untested edge cases and OWASP issues. Senior engineers spend their review time on business logic and architecture.

Yes. CloudAEye ships a Terraform bundle that deploys onto Amazon ECS inside your own AWS account, including an ECS cluster, VPC, load balancer, SQS, and private MongoDB and ChromaDB services. Source code never crosses your perimeter. Amazon ECS is the supported orchestrator today.

Yes! You get 50 free credits every month, equivalent to 50 code reviews. Credits cover code reviews, pre-commit checks directly within coding agents (Claude Code, Codex, and Cursor), PR description, and repository tokenization. No credit card required.

Start with the next change

AI will keep writing more code.
Make every change prove it is ready.

Start free Read the docs

$50 free credits per month. No credit card.