Struct

Use Case 1: Reducing On-Call Fatigue via Automated Root Cause Analysis

Problem: Engineering teams are often overwhelmed by "alert fatigue." When a critical error occurs, on-call engineers must manually comb through logs, Sentry alerts, and Datadog metrics to find the source of the issue, often losing hours of sleep or productive time in the process.
Solution: Struct acts as an automated on-call agent that proactively investigates engineering alerts as they happen. It pulls context from the entire observability stack to identify the root cause and assess customer impact before an engineer even starts their investigation.
Example: A Sentry alert triggers at 3:00 AM. Instead of the engineer manually searching through logs, Struct automatically replies to the Slack alert thread with a root cause analysis identifying a specific faulty database query introduced in a recent GitHub commit.

Use Case 2: Managing "Alert Storms" with Intelligent Deduplication

Problem: During a major system outage, a single underlying issue can trigger hundreds of redundant alerts across different channels (Sentry, Cloud logs, Datadog). This "noise" makes it difficult for teams to communicate and find the actual starting point of the failure.
Solution: Struct intelligently dedupes related issues across the entire stack. It groups related notifications into a single incident thread, ensuring the team stays focused on the primary problem rather than being buried under a mountain of repetitive alerts.
Example: An API gateway failure causes 50 different microservices to report errors simultaneously. Struct recognizes these are all symptoms of the same gateway issue and consolidates them into one Slack discussion, preventing the engineering channel from being flooded.

Use Case 3: Accelerating Bug Remediation with One-Click Fixes

Problem: Even after a bug is identified, the process of switching to a code editor, creating a branch, writing a fix, and ensuring it builds correctly takes significant time. This delays the "Time to Resolution" (TTR).
Solution: Struct bridges the gap between triaging and fixing by offering a "Fix with one click" feature. It can automatically generate Pull Requests (PRs) that are designed to build cleanly, or it can hand off the full context of the incident to a secondary coding agent.
Example: Once Struct identifies that a null pointer exception is causing an app crash, the engineer clicks a button in the Struct interface. Struct automatically generates a GitHub PR with the necessary code changes and context, ready for review and deployment.

Use Case 4: Deep Dive Investigations for Complex, Stateful Errors

Problem: Some bugs are "stateful" or complex, meaning they only occur under specific sequences of events. Understanding these requires a side-by-side comparison of incident timelines, commit histories, and log queries, which is tedious to do manually.
Solution: Struct provides a "Dive Deeper" feature within Slack. It allows engineers to test alternative hypotheses and explore deep-stack data—including incident timelines and log queries—without leaving their communication platform.
Example: An engineer is unsure if a memory leak was caused by a new feature or a dependency update. They use Struct in Slack to query specific log patterns and view a side-by-side timeline of recent deployments versus memory usage spikes to confirm the hypothesis.