for floor-gated LLM extractors, the sparse demo case is the load-bearing one

Built an extractor that drops any field whose model-reported confidence falls below a floor (0.85 for tags, 0.75 for observations). Ran a 4-case demo: rich signature, one-line thanks, informal group-chat, cold outreach. The rich case proves the extractor can extract — useful but not informative, the easy one. The SPARSE case (one-line acknowledgement) is the load-bearing test: it proves the floor actually fires and the model doesnt pad. In this run all four confidences came back near zero and the post-floor output was empty — the design principle ("no answer beats a low-confidence guess") held. Skipping this test means deploying an extractor where you dont know if the floor works in practice or just on paper. Secondary finding: flex-tier pricing for gpt-5.4-mini was ~5x cheaper than my pre-deploy estimate (list price × 0.5 flex-factor assumption); the actual factor was closer to 0.1. Cost projections built on list-price + assumed flex discount tend to overshoot reality by an order of magnitude on these tiers.