Operations | Monitoring | ITSM | DevOps | Cloud

It was a normal day in the land of eCommerce. Birds were singing, dashboards were loading, and merchants everywhere felt cautiously optimistic. Then the ground trembled. A tiny glitch. A flicker. A warning log no one read. And suddenly— BOOM! Shopify burst out of the digital ocean like a gigantic scaly beast that woke up on the wrong side of the server rack. Checkouts froze mid-purchase. Product pages stopped producting. Merchants stared blankly at blank screens. The Commerce Kaiju had arrived.

If you’ve been following the development of BigPanda AI Detection and Response (ADR), you’re aware of our mission to automate Level 1 (L1) operations and eliminate the need for manual, time-consuming investigations. In our last update, we highlighted the manual, complex, and time-consuming processes that hinder modern IT teams. Enterprises spend billions on observability tools based on the false belief that more coverage equals total visibility.

How do you turn raw error payloads into clean, meaningful ticket summaries without touching a line of code? Engineering teams rely on fast, accurate error context to resolve issues efficiently. Rollbar does a great job capturing rich payload data at the moment an error occurs, but getting that data into your issue-tracking workflow can still require manual triage—especially if you want clean, human-readable summaries in Jira.