We’re excited to announce the general availability of Relvy’s integration to Atlassian Confluence to use as a source to help guide our agentic AI to troubleshoot. And to show our customers relevant content to help debug an existing incident Relvy is troubleshooting. We built Relvy to ingest documentation to assist our agentic AI in troubleshooting along with ingesting logs, metrics, code and traces. This release further adds to Relvy’s integrations.  

‍Integrating to Confluence means Relvy can take advantage of your company’s existing runbook and post-mortem documentation to instantly become your expert on-call engineer. 

In our onboarding process in addition to connecting to various data sources such as Amazon Cloudwatch, Datadog, Observe, Slack and others we have a second step which encourages our clients to enter how their on-call engineer tackles issues as well as most importantly, which data sources are used to do so. We ingest customer documentation as runbooks, post-mortems or other notes using Relvy to create a customer specific runbook on the client’s specific debugging process. We’ve found this runbook is best created by starting with our client’s own runbooks which we can automatically ingest from documents and now most notably from Confluence. This ensures Relvy/ our AI agent behaves as an on-call engineer would for the customer’s specific environment following the customer’s specific troubleshooting methodology.

As Atlassian Confluence has become an enterprise standard for documentation, it was inevitable that we integrate Relvy’s automated troubleshooting to Confluence. 

We’ve paired our cost effective custom tuned language models which operate at 1/200th the cost of existing foundational models to make 24/7 agentic AI monitoring and debugging a reality. Get started instantly and see how Relvy can drastically reduce debugging time and costs, transforming your engineering processes today. 

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