From Silicon to System: Building AI That Works at Scale

What does it take to turn a powerful AI model into a dependable business system?

In this episode of AI Lab, we explore the full journey from compute and inference to workflows, quality controls, and measurable results. We unpack why a faster model can still sit inside a slow application, how retries and human review change the economics, and why cost per successfully completed task matters more than token price alone.

Topics include choosing between managed APIs and self-hosting, finding bottlenecks through end-to-end observability, evaluating what agents actually accomplish, and enforcing data permissions throughout the workflow.

The practical starting point: define what successful work looks like, measure one representative workflow, and use that evidence to guide your infrastructure decisions.