Struct provides an AI‑driven platform that automatically investigates on‑call alerts by cross‑referencing logs, metrics, traces, and code repositories. It delivers root‑cause analysis, impact assessment, and suggested fixes directly within tools like Slack, enabling engineers to resolve incidents faster and reduce manual triage time. The service is offered via a credit‑based subscription model with tiered plans for individual users up to enterprise deployments.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams spend significant time manually triaging alerts by stitching together logs, metrics, traces, and code repositories. This manual process delays incident resolution, increases on‑call fatigue, and often leads to inconsistent root‑cause analysis. Scaling alert investigation across fast‑moving product cycles becomes increasingly untenable.
Solution
Struct provides an AI‑driven on‑call automation platform that ingests data from observability tools, cloud log services, and version‑control systems to generate immediate root‑cause analyses. When an alert fires, the system cross‑references relevant logs, metrics, distributed traces, and recent code changes, then returns a concise impact assessment and suggested remediation steps. Engineers can invoke investigations on demand via Slack mentions or let the platform run automatically for every incoming alert. The output includes incident timelines, commit histories, and ready‑to‑merge pull‑request drafts, enabling rapid handoff or automated fixes. Over time, Struct builds on‑call intelligence from past investigations, improving accuracy and reducing false positives. All data remains logically isolated, with SOC 2 Type II and HIPAA compliance, ensuring enterprise‑grade security.
Target Audience
The primary customers are SRE, DevOps, and engineering teams at fast‑growing SaaS or fintech companies that rely on observability platforms for production monitoring. It also serves product engineering groups needing consistent, automated incident triage across multiple services.
Features
- LLM‑powered analysis engine that correlates logs, metrics, traces, and code changes in real time
- Native integrations with Sentry, Datadog, major cloud log providers, and CI/CD platforms such as GitHub
- Slack, Linear, Asana, and other workflow connectors for automated incident reporting and @‑mention triggers
- Auto‑generated investigation reports with incident timelines, root‑cause summaries, and one‑click PR creation or handoff to a coding agent
- On‑call intelligence layer that learns from historical alerts to prioritize and refine future investigations
- Credit‑based usage model with granular control (15–30 credits per investigation) and priority queueing for higher‑tier plans
- Enterprise security features: logical data isolation, end‑to‑end encryption, RBAC, SSO/SAML, and optional on‑prem sidecar deployment