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Thinking behind the systems.
Chapters from our case-study series on a regulated document-AI platform, each taking one engineering problem in depth, alongside practical guides for planning AI work.
How we built a pipeline that reads every page of a 10,000-page medical record, proves it did, and scales with the work rather than the CPU.
How we run 2.7 billion+ tokens through production with full cost visibility, governed capacity and resilient telemetry.
A practical starting point for founders and technology leaders: describe the task, existing systems, data and first-release questions before choosing the implementation.