Meet Cheer

Cheer is a way to ensure quality and trust in AI-generated code and tests prior to releasing. We believe AI is here to stay and will dramatically improve developer productivity — but only when balanced with the right policies and intelligent release gates.

AI is transforming how we build software

Today's developers are writing code faster than ever. GitHub reports that 41% of all code is now AI-generated. Tools like Copilot, Claude, and Cursor have become indispensable parts of the modern development workflow.

This acceleration is incredible — but it comes with new challenges. When AI writes your code, how do you ensure it meets your quality standards? How do you maintain security? How do you know when it's safe to ship?

Human-in-the-loop governance is the answer. Smart checkpoints that pause automated workflows when human expertise is critical, while letting AI handle the repetitive work it excels at.

"We needed a way to harness AI's speed without sacrificing the quality and security our customers depend on. Cheer's human-in-the-loop governance gives us that confidence."

— Platform Team Lead, Fortune 500 Company

AI Development Growth

AI-Generated Code41%
Developer Adoption76%
Security Failures45%

The trust gap is killing productivity

AI makes us faster, but without proper guardrails, teams lose confidence and slow down

⚠️

The Problem

  • ×45% of AI-generated code fails security tests
  • ×Only 33% of developers trust AI code output
  • ×66% spend more time debugging "almost-right" AI suggestions
  • ×800% increase in code duplication from AI tools
  • ×No visibility into which code was AI-generated

Our Solution

  • Human-in-the-loop governance at critical decision points
  • Comprehensive AI contribution tracking and attribution
  • Automated security and compliance verification
  • Policy-driven release gates with configurable thresholds
  • 0-10 quality scoring across all components

How AI Governance Works

Multi-source detection, human review verification, and transparency at every step

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Multi-Source Detection

  • Git-based tracking (Co-authored-by tags, commit patterns)
  • Code pattern analysis (boilerplate detection, style consistency)
  • Direct AI tool integrations (Copilot, Cursor, Claude)
  • Developer self-reporting via git hooks
👥

Human Review Verification

  • Active review metrics (time spent, comment depth)
  • Review quality scoring based on thoroughness
  • Historical reviewer accuracy tracking
  • Cross-reviewer agreement analysis
📊

Extended AI Metrics

  • AI Suggestion Acceptance Rate (accepted vs. modified)
  • AI Code Stability Score (bug rate comparison)
  • Critical Path Exposure (AI code in critical systems)
  • Human Enhancement Score (improvements to AI suggestions)

Our principles

The values that guide how we think about AI, quality, and developer experience

🤝

Human + AI Partnership

AI should amplify human intelligence, not replace human judgment. Our platform ensures AI handles repetitive tasks while humans make critical decisions about architecture, security, and quality.Smart checkpoints, not roadblocks.

📊

Transparency First

Every piece of code should have a clear provenance. Teams should know exactly what was AI-generated, what was human-written, and what was collaboratively created. No black boxes, no hidden AI contributions.

Velocity with Confidence

Speed means nothing without quality. We believe teams should ship fast and often, but only when they have confidence their code meets security, quality, and compliance standards.

The future is collaborative

We're building toward a future where AI and humans work together seamlessly. Where AI generates the first draft and handles the heavy lifting, while humans provide the wisdom, creativity, and quality oversight that creates truly great software.

This isn't just about tools — it's about creating a development culture where teams can embrace AI's productivity gains while maintaining the trust and quality their users depend on.

Ready to bring trust to your AI development?

Join the teams already using Cheer to build better software with AI