Jihye Park

At Endowus, hundreds of Sentry notifications flow daily into dedicated Slack alert channels. These alerts are essential for reliability and incident response, but keeping up with the volume is a different challenge altogether. We’ve written about our Sentry & Slack alerting setup in a previous post: Solving the Shared Ownership Alerting Challenge. Our initial setup worked well, but as alert volumes grew, the strain started to show. During a particularly busy period of releases and activity, working closely with our engineering teams as a Tech Program Manager, I could see how much time and attention was being diverted from product development into manually reviewing alerts. Useful signals were present — recurring errors, gradual spikes, subtle correlations — but extracting them required sustained focus across multiple Slack channels and time windows. Although I’m not a developer, I wanted to help the team solve this. Using AI-assisted development tools, I built an initial working prototype myself, iterating on data extraction, formatting and analysis logic. That first version made the concept tangible: structured interpretation could reduce review effort without disrupting existing monitoring workflows. Building the prototype also sharpened the problem definition. The alerts themselves were not broken: they were working exactly as intended. What was missing was a structured way to interpret them at scale.