The missing governance layer for AI in software development.
AI is transforming your SDLC—Copilot, Cursor, Devin, CodeRabbit, auto-fix bots. But each tool operates independently, creating code bloat, hallucinations, and ungovernable drift.
SignalCheck provides unified, deterministic governance across all AI tooling in your development workflow.
Enforce code quality. Catch hallucinations. Gate automation. Make AI contributions safe, compliant, and auditable.
The AI tool fragmentation problem
Your team uses a mix of AI tools: GitHub Copilot, Cursor, Windsurf, Devin, Sweep, CodeRabbit, Dependabot. Different models (GPT-4, Claude, Gemini), different contexts (MCP servers, RAG systems), different standards.
AI generates bloat, anti-patterns, and security vulnerabilities. One developer's AI generates clean code, another's generates technical debt.
AI proposes changes that look correct but aren't. False API usage, incorrect assumptions, plausible-but-wrong code.
AI auto-commits to protected branches, triggers risky deployments, creates cascade failures—without proper guardrails.
Can't answer "which AI tool committed this bug?" No unified governance. Risk compounds over time.
Unified governance for all AI tooling
SignalCheck doesn't replace your AI tools—it makes them work together safely. It's the policy fabric that ensures Copilot, Cursor, Devin, CodeRabbit, and every other AI tool operates within the same safety boundaries.
Whether code comes from Copilot or Cursor, whether it's GPT-4 or Claude, whether it's a coding assistant or agentic platform—SignalCheck evaluates it through a single, consistent policy engine.
Block bloat, anti-patterns, and security vulnerabilities before they enter the codebase. Set complexity limits, file size thresholds, test coverage requirements—enforced uniformly across all AI tools.
Require minimum confidence thresholds. Validate against known failure signatures. Escalate uncertain changes for human review. Prevent plausible-but-wrong code from reaching production.
Control auto-commits, merges, and deployments with context-aware policies. Different rules for feature branches vs main, dev vs production, low-risk vs high-risk changes.
Same inputs produce identical decisions every time. No LLMs in the decision path. Export findings as SARIF 2.1.0 for security tooling. Prove compliance with reproducible audit trails.
How it works
Developers love SignalCheck because it enables AI to accelerate development without sacrificing safety. It's not a gate, it's guardrails. AI proposes. Policy decides. SignalCheck judges.
AI proposes contribution
AI generates code, proposes a fix, suggests a dependency update, or triggers automation—from any tool in your stack.
SignalCheck evaluates
Contribution is evaluated against policy: code quality standards, complexity limits, confidence thresholds, branch protections, environment constraints.
Verdict rendered
Allow (safe to proceed), deny (violates policy), or escalate (use this safer alternative instead).
Three ways to integrate
curl -X POST https://signalcheck.ai/v1/check \
-H "Content-Type: application/json" \
-d '{
"action": "commit",
"branch": "main",
"confidence": 0.92
}'
// Response
{
"decision": "denied",
"reason": "Direct commits to main not allowed",
"alternative": "create_pull_request"
}# Check a proposed action
signalcheck agent check \
--context action.json \
--policy policy.yaml
# Output: deterministic decision with evidence
✓ ALLOWED: Patch update on feature branch
Policy: allow_patch_updates_on_feature
Confidence: 0.95
Response time: 12msapiVersion: signalcheck.ai/v1
kind: AutomationPolicy
metadata:
name: safe-automation
spec:
# Allow low-risk actions
allow:
- action: commit
branches: ["feature/*"]
confidence: { min: 0.85 }
# Escalate risky actions
escalate:
- action: commit
branches: ["main"]
alternative: create_pull_request
# Deny unsafe actions
deny:
- action: force_push
reason: "Prevents destructive operations"Real-world use cases
Example: Unified governance across AI tools
The scenario
Your team uses Copilot for code completion, Cursor for refactoring, Sweep for issue resolution, CodeRabbit for reviews, and Renovate for dependencies. Each developer has different IDE setups, MCP servers, and context configurations.
Without SignalCheck
Each tool operates with different quality standards. Copilot generates 50-line functions, Cursor generates 200-line functions—no consistency. Some tools auto-commit to main, others create PRs—no uniform policy. Can't audit which tool introduced a bug. Risk compounds as tools work independently.
With SignalCheck
All AI contributions evaluated through single policy engine. Same complexity limits apply whether code comes from Copilot or Cursor. Consistent branch protection regardless of which tool proposes changes. Track which tool, model, and context produced each contribution. Different tools, same safety boundaries—eliminate drift.
The result
Your team uses the best AI tools for each task while maintaining consistent governance. Add new AI tools without rewriting governance rules. Scale AI adoption safely.
Technical guarantees
Current status
SignalCheck is early-stage and design-partner driven. Core engine is complete. HTTP server and CLI are production-ready. This is infrastructure tooling, not a polished SaaS.
Let's talk
SignalCheck is for teams who are:
- •Using multiple AI coding tools (Copilot, Cursor, etc.) without unified governance
- •Seeing code quality degradation from unchecked AI contributions
- •Deploying AI agents but need compliance and audit controls
- •Responsible for security, platform engineering, or developer experience