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Applied AI and automation: what it actually does inside a working system, and where a human stays in the loop.
Automation earns its keep on high-volume, well-defined steps. It fails quietly on judgment calls dressed up as rules. Here is how to tell the two apart before you build.
RAG is not a magic fix for a model that does not know your data. It is a specific architecture with real tradeoffs. Here is what it actually does and when it is the wrong tool.
An AI feature is not done when the model gives good answers in a demo. It is done when it has guardrails, a way to measure quality over time, and a clear point where a human intervenes.
A short list of questions worth answering honestly before an automation project starts, based on where these projects have gone wrong across the operations we have reviewed.
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