The gap between an impressive demo and a dependable product is where most AI initiatives stall. In this piece we break down the engineering practices — evaluation harnesses, guardrails, observability, and cost controls — that turn a promising model into a system your business can rely on.
From pilot to product
Start with a measurable outcome, instrument everything, and treat the model like any other dependency: versioned, tested, and monitored.
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