Best for
- Systems debugged today by adding print statements and redeploying
- Alert channels everyone muted months ago
- Knowing about outages before the customers do
What you give it
- Your system, its critical flows, and your current monitoring (if any)
- What keeps you up at night: the failures you most need to catch early
What you get back
- Instrumentation where it pays: structured logs, metrics and traces on the paths that matter
- Dashboards organised by question ('is checkout healthy?') not by host
- Alerts rebuilt on symptoms with runbook links — and the mute-worthy ones deleted
How it works
- Starts from the questions you must be able to answer at 3am, and works back to the data needed.
- Instruments the critical paths first: structured events, correlation ids across services, the metrics that define healthy.
- Builds dashboards per question with the four signals that matter: rate, errors, duration, saturation.
- Rebuilds alerting on user-visible symptoms with severity honesty — pages for pain, tickets for trends.
- Audits the existing noise and deletes it with reasons, because muted alerts are worse than none.
Example
You: We find out about problems from customer emails. Fix that.
Result: The four critical flows instrumented end-to-end with correlation ids; a per-flow health dashboard (rate, errors, latency percentiles); nine symptom alerts each linking a runbook — and 31 of the old 40 alerts deleted with reasons. The next incident was caught by the checkout alert eleven minutes before the first customer email.
Limits — please read
- It works within your monitoring stack; new platforms are a proposal with a case.
- Observability costs (volume, cardinality, retention) are engineered deliberately and shown, not discovered on the invoice.
- Instrumentation shows what you measured; the first incident after setup usually teaches one more gap — the loop includes learning from it.