Struct

Product Introduction

1. Definition: Struct is an advanced AI on-call agent and automated root-cause analysis (RCA) platform designed for modern engineering teams. It functions as a specialized AIOps (Artificial Intelligence for IT Operations) solution that autonomously investigates production incidents by synthesizing data from observability tools, log providers, and version control systems.

2. Core Value Proposition: Struct exists to eliminate the manual labor associated with on-call rotations by providing proactive incident triage. By automating the correlation between engineering alerts and the underlying codebase, Struct significantly reduces Mean Time to Resolution (MTTR) and mitigates alert fatigue. The platform enables developers to resolve complex bugs before they even open their laptops, leveraging a composable system that integrates seamlessly into existing DevOps workflows.

Main Features

1. Automated Root-Cause Investigation Engine: Upon receiving an alert from platforms like Sentry or Datadog, Struct immediately initiates an autonomous investigation. It cross-references logs, metrics, and distributed traces with the specific lines of code in the repository. The engine analyzes the impact of the issue and generates a detailed report including the root cause, an impact analysis, and a suggested code fix.

2. Cross-Stack Context Integration: Struct serves as a centralized intelligence layer for the entire DevOps stack. It integrates with leading observability platforms (Datadog, Sentry), cloud logging services, and project management tools (Slack, Linear, Asana, GitHub). This allows the agent to ingest context from disparate sources, providing a holistic view of the system's state during an incident without requiring manual context switching by the engineer.

3. Intelligent Remediation and Build Agent: Beyond diagnosis, Struct facilitates rapid resolution through its AI Build Agent. It can automatically generate Pull Requests (PRs) that are verified to build cleanly. For complex issues requiring human intervention, it provides a comprehensive handoff package—including incident timelines and commit histories—to another coding agent or a human developer, ensuring no context is lost during the transition.

Problems Solved

1. Pain Point: Manual Triage and Alert Fatigue: Engineering teams often spend hours manually querying logs and tracing errors across microservices. Struct addresses this "high-toil" environment by automating the initial investigation phase, ensuring that every alert is greeted with an actionable analysis rather than just a notification.

2. Target Audience: The primary users include Site Reliability Engineers (SREs), DevOps Professionals, Full-stack Software Engineers, and Engineering Managers. It is particularly essential for fast-moving startups and enterprise teams operating under SOC2 or HIPAA compliance requirements who need to maintain high availability.

3. Use Cases: Struct is essential for resolving production regressions after a deployment, identifying intermittent bugs that are difficult to replicate, automating the creation of internal incident post-mortems, and providing 24/7 coverage for global on-call rotations where immediate human response might be delayed.

Unique Advantages

1. Differentiation: Unlike traditional observability dashboards that only display data, Struct interprets data. It moves beyond "monitoring" into "active resolution." Compared to generic LLM chatbots, Struct uses "On-call Intelligence," which builds a memory of past issues and investigations to improve its diagnostic accuracy over time within a specific technical environment.

2. Key Innovation: The core innovation lies in the secure, logic-isolated architecture. Struct is designed with a "Secure by Design" philosophy, being fully SOC2 Type II and HIPAA compliant. Crucially, it ensures that customer data is never used for training third-party models, allowing enterprise-grade organizations to leverage LLM-powered RCA without compromising proprietary codebase security.

Frequently Asked Questions (FAQ)

1. Is Struct secure for sensitive codebases and data? Yes. Struct is SOC2 Type II and HIPAA compliant. All data is logically isolated and encrypted. A key security differentiator is that Struct and its data providers do not use your proprietary code or logs for model training, ensuring your intellectual property remains private.

2. How does the Struct credit system and pricing work? Struct uses a credit-based pricing model. A single automated investigation typically consumes between 15 and 30 credits. The "Free" tier includes 100 credits per month, while the "Pro" and "Max" tiers offer higher volumes (220 and 2,500 credits respectively) with additional features like the Struct build agent and priority support.

3. Which observability and developer tools does Struct support? Struct supports a wide range of leading industry tools including Sentry, Datadog, Slack, Linear, Asana, and GitHub. It also integrates with various cloud log providers, allowing it to ingest telemetry and context from almost any modern infrastructure stack.

4. Can Struct automatically fix bugs in production? Struct can generate suggested fixes and create Pull Requests that are designed to build cleanly. While it automates the investigative and drafting phases, it is built to work within your existing workflow, allowing engineers to review and approve PRs or hand off tasks to other automated build agents with full context.