Superlog is an AI agent platform that automatically investigates production alerts from tools like Sentry, Datadog, and Slack, tracing each issue through a company's codebase to identify root causes and suggest fixes. The platform connects to observability and developer tools, opens pull requests for real issues, and filters out noise to reduce alert fatigue. It provides complete context by accessing code, logs, and production telemetry, enabling engineers to resolve bugs faster.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams face alert fatigue from the constant stream of production alerts generated by observability tools like Sentry and Datadog. Investigating these alerts requires manually sifting through logs, stack traces, and code to identify root causes, which is time-consuming and distracts engineers from higher-value work. This manual debugging process delays incident resolution and increases operational burden on on-call teams.
Solution
Superlog is an AI agent platform that automatically investigates production issues by connecting to a company's existing observability and communication tools. When an alert is triggered, Superlog traces it through the codebase, analyzes logs and telemetry, and returns the root cause, supporting evidence, and a recommended path to resolution. The agent replies directly in Slack, opens pull requests for genuine bugs, and filters out false positives to reduce noise. It integrates with Sentry, Datadog, Slack, GitHub, and Linear, and can access context from Notion, AGENTS.md files, and custom MCP servers to inform its investigations.
Target Audience
Primary customers are software engineering teams, particularly on-call engineers and engineering managers at startups and scale-ups who use observability platforms like Sentry and Datadog and want to reduce time spent on manual debugging.
Features
- Automated alert investigation that traces issues through the codebase and returns root cause, evidence, and resolution path
- Slack-native workflow that replies to alerts and provides follow-along updates during incident response
- Pull request generation for confirmed bugs with CI checks and PR review approval built into the workflow
- Integration with Sentry, Datadog, Slack, GitHub, and Linear for seamless data flow across the development stack
- Access to codebase context including AGENTS.md, CLAUDE.md, custom prompts, and MCP servers for more informed analysis
- Noise filtering that distinguishes real issues from false positives, reducing alert fatigue for on-call teams