Ai

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.