Building an AI demo is easy. Shipping an AI system that runs reliably in production, day after day, under load, within budget, and with observability, is hard. Many teams discover this gap only after their impressive proof-of-concept collapses when exposed to real users, real data, and real operational constraints.
“Building an AI demo is easy. Shipping an AI system that runs reliably in production… is hard.”
Production AI systems are more than models. They are distributed systems that must handle failures, control costs, evolve…
