Rootly is the AI-native on-call and incident management platform carefully crafted for your entire organization to prevent and resolve incidents faster, without ever leaving Slack—trusted by thousands of companies like LinkedIn, NVIDIA, Replit, Elastic, Canva, Clay, Tripadvisor, Grammarly, Mistral AI, Okta, Glean, and many more.
With Rootly, you can focus on the incident response, knowing your process is automated, consistent, and reliable.
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Centralize knowledge and decisions to reduce your time to recovery—connect your stack using the tools you know and love, like Sentry, Linear, Jira, Backstage, Datadog, and hundreds more, all within Slack.
:loudspeaker: Be confident the right people will be notified with simplified on-call schedules and escalation policies, including smart overrides, live call routing and more. You can even sync your schedules with Slack user groups.
:busts_in_silhouette: Easily keep everyone aligned and up to date with auto generated, contextual summaries that include clear timelines and who’s working on what—just @Rootly to draft comms, assign tasks, generate summaries, and more.
:stopwatch: Resolve incidents faster, with less effort and fewer people using automated root-cause analysis that’s complete with similar incidents, suggested fixes, and next steps.
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Keep post-incident learning consistent and save hours—AI generated retrospectives complete with timelines and context, incl. follow up tasks and checklists—sent to tools you love like Confluence, Docs, and Notion.
:racing_car: Identify patterns and drive continuous improvements—understand your incident trends; responder burnout, wellbeing, on-call readiness, and more from advanced reporting, metrics, and insights.
*:woman-lifting-weights: Make Rootly and your process work for you—*automate your entire process with powerful, no-code workflows and custom fields.
To get started for free, add the Rootly app to Slack, or get started at [
rootly.com](
http://rootly.com).
Disclaimer: [Rootly AI](
https://rootly.com/) uses Large Language Models (LLMs) that can have the potential to produce inaccurate information.