Struct

Most observability tools are very good at telling teams that something broke. Figuring out why it broke is usually the slow and painful part.

Struct is built around that exact problem. The platform uses AI to investigate engineering alerts by analyzing logs, metrics, traces, and source code together, helping teams identify likely root causes much faster than traditional manual debugging workflows.

Instead of bouncing between dashboards, infrastructure tools, and monitoring systems during an incident, engineers can use Struct as an investigation layer that connects operational signals into a more coherent explanation of what actually happened.

Designed for real DevOps workflows

Struct is not trying to replace an engineering team’s existing observability stack. The product is designed to plug into current infrastructure and work alongside tools companies already use for monitoring and incident response.

That makes the platform especially relevant for modern cloud environments where production systems generate huge amounts of telemetry data across distributed services. During incidents, the challenge is rarely lack of information. The challenge is turning that information into actionable understanding quickly enough to reduce downtime.

By combining infrastructure signals with code-level context, Struct tries to shorten the gap between:

receiving an alert
identifying the failure chain
understanding the root cause
and resolving the issue

The result is a workflow that feels less like manually searching through monitoring data and more like collaborating with an AI system focused specifically on incident investigation.