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Their Kubernetes cluster lived in one AWS region. When eu-west-1 had a 47-minute outage, they processed zero transactions. The CTO called us the next morning. Their enterprise sales cycle was frozen because every prospect asked the same question: "What happens when your region goes down?" They had 6 weeks to answer it before a Q2 pipeline review.

Black Friday 2023 was a disaster. Their monolithic PHP storefront collapsed at 09:14 CET. Cart abandonment hit 67%. The CEO watched revenue drop in real time on a dashboard in the office. They spent €180K on emergency cloud scaling that day, most of it on over-provisioned VMs that sat idle by Monday. For 2024, they had two options: hire a 40-person platform team, or fix the architecture. They chose the latter and gave us 14 weeks.

Their AI model for detecting early-stage diabetic retinopathy worked beautifully in the lab: 94.7% accuracy on their validation set. In production, it failed silently. Nurses stopped trusting it after 3 months because it flagged healthy patients 12% of the time on real hospital data — a training-serving skew they did not detect until a clinician complained. They were 8 months from a regulatory submission and had no reproducible pipeline, no audit trail, and no way to explain why the model made a specific prediction.