Software breaks, not just at the code level, but across the systems that run it. When something goes wrong, engineers are left piecing together scattered clues: logs, metrics, traces, configs, and assumptions. Debugging becomes slow, painful, and costly.

Syncause exists to change that.

We're building an AI-powered debugging assistant that unifies code debugging and system root cause analysis into one seamless experience. By capturing deep runtime context and combining it with intelligent root cause reasoning, Syncause helps engineers instantly understand why failures happen before they escalate into incidents.

• Debugging should be intelligent
  No more guesswork. Every failure leaves a trace; we make it visible and explainable.

• Observability should be actionable
  Data alone isn't enough; insights must drive confident decisions.

• Engineers should build, not firefight
  Debugging shouldn't be the bottleneck to shipping and scaling great products.

• Clear, contextual insights from code to infrastructure
• AI-driven root cause explanations you can trust
• Faster recovery and higher developer productivity
• Reduced user impact and operational stress

To pioneer a new era of autonomous debugging where software understands itself well enough to help fix its own failures.

We believe that AI SRE Agents must be transparent and measurable. In this GitHub organization, we openly share:

• Evaluation results of different AI models and algorithms
• Benchmark reports measuring accuracy, interpretability, and cost efficiency across test datasets

You can explore the latest benchmark results here: 👉 Syncause Benchmark Repository