observability

A client's AI features were burning through their OpenAI budget 3x faster than projected. Adding OpenTelemetry's GenAI semantic conventions revealed the problem wasn't what anyone expected.

A client was confident about how their services talked to each other. Then we instrumented the system with OpenTelemetry and found out what was actually happening.

A client had six monitoring tools and still couldn't diagnose a production incident in under an hour. The problem wasn't the tools — it was what happens when observability grows by accretion instead of design.

A client's notification queue was draining normally and all dashboards showed green. But three weeks of transactional emails had vanished into a catch block nobody thought to monitor.

A client's on-call rotation was burning people out — not because of real incidents, but because of noise. Here's how we cut alerts by 87% without missing anything that actually required a human.

We built a canary deployment pipeline with automated rollbacks. It still let a bad release through to 100% of users. Here's what went wrong.

A debugging deep dive into replacing wall-of-text logs with structured logging and trace IDs — and how it cut our mean time to resolution from hours to minutes.