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What distinguishes an enterprise feasibility spike from a standard machine learning benchmark in forward deployed engineering?

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intermediate 45 min

Day 12: Test Feasibility Against Customer Evidence

Test Feasibility Against Customer Evidence

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Consider the record inspection logic from the Northstar feasibility runner. What failure state does this function catch before computing event freshness?

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Northstar's feasibility spike on 200 historical carrier records yields: 98% schema completeness, 94% event code mapping, but only 64% freshness within the 15-minute operational limit (the target was 90%). What is the correct engineering decision for the FDE team?

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