Cutting incident response time by 68% with an AI-powered observability dashboard

01 / The Problem

NeuralOps' ML pipeline was generating accurate, early-warning signals — but those signals were surfaced through a CLI and a tangle of Slack alerts. Engineering teams were experiencing alert fatigue: an average of 340 Slack notifications per engineer per day, with no way to understand severity, cluster related events, or track resolution progress. Prospects loved the demo, but conversion from trial to paid was stuck at 9% because the product required too much setup expertise to feel immediately valuable. The core challenge was clear: the intelligence existed; the interface to make it legible did not.

02 / Our Approach

We started with a five-day Discovery Sprint embedded with NeuralOps' engineering and customer success teams. We interviewed six engineering managers and four site reliability engineers across trial accounts, and shadowed two on-call rotations to understand how incident response actually worked in practice — not how it was documented. Three findings shaped everything: first, on-call engineers made triage decisions in under 90 seconds, meaning the dashboard had to communicate severity and blast radius at a glance. Second, context collapse was the primary source of cognitive load — the same underlying issue could generate 40+ discrete alerts with no visible relationship between them. Third, teams wanted to involve their wider engineering org in post-mortems but had no sharable artefact to anchor the conversation. We also ran a competitive audit across Datadog, Grafana, and PagerDuty — identifying where density of information became an obstacle rather than an asset.