Struct is an AI tool that assists engineering, customer support, design, and product teams with automated incident investigation and triage. Functioning as an on-call agent directly within Slack, Struct integrates full-stack context to investigate alerts, perform automated root cause analysis, and determine customer impact. When system errors happen, the platform intelligently deduplicates alerts from multiple sources to eliminate alert fatigue and consolidates related notifications into unified incident threads. It also automates on-call runbooks and analyzes historical issue context to diagnose recurring or complex problems. Beyond investigation, Struct helps engineers remediate issues by generating pull requests with a single click, preparing clean-building code fixes, or handing incident context over to secondary coding agents. Users can also conduct deep-dive investigations, test diagnostic hypotheses, and analyze incident timelines without leaving their chat workflow. The pricing model for Struct is currently unlisted.
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.
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.
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.
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.
Target audience: Best for: Customer support teams, Design teams, Product managers
Pricing: Unknown · Categories: Audio Editing, Customer Support, Design
Tags: audio editing, Customer Support, design assistant, human resources, image generator, research, seo, social media assistant, text to speech
Struct is an AI-powered investigation sidekick that operates within Slack. It automates root cause analysis for engineering alerts, evaluates customer impact, groups redundant notifications through intelligent deduplication, and generates pull requests to resolve identified software bugs.
Struct monitors incoming error alerts, pulls context across the observability stack, and identifies the underlying cause of failures. It deduplicates alerts during incident storms, automates runbooks, provides historical issue intelligence, and offers one-click pull request generation to fix verified errors.
During major outages, Struct uses intelligent alert deduplication across the software stack. It detects when multiple errors from services, logs, or error monitoring tools stem from the same root issue and consolidates them into a single incident thread in Slack.
Struct is built for engineering teams, on-call responders, customer support teams, design teams, and product managers who need to investigate operational errors, triage alerts, and resolve software incidents faster without manual log searches.
Yes. Struct includes a one-click pull request generation feature. Once an error root cause is identified, users can prompt the tool to generate a GitHub pull request with the relevant code changes or pass full incident context to another coding agent.