Schemas from the consumer
Derive fields from what the downstream system reads, with absence semantics and normalization contracts reviewed before extraction code.
Premium skill
Production structured-output pipelines on the AI SDK: schemas designed from downstream needs, bounded validation and repair, confidence routing to human review, and per-field accuracy on a golden document set.
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Derive fields from what the downstream system reads, with absence semantics and normalization contracts reviewed before extraction code.
Schema validation is the write gate. Cap repair attempts and dead-letter documents that exhaust them.
Golden sets report precision and recall per field and document class, not a blended 90 percent score.
Thresholds come from measured error cost. Low-confidence values never silently reach production writes.
Pairs with Build AI SDK Agent Evals for measurement conventions and Better Generative UI when you stream partial objects into review surfaces. Routes degraded scans and corpus-scale parsing to Ingest Messy Documents rather than absorbing that method here.
An invoice pipeline that refuses to write until the schema parses and totals balance.
A team that thought extraction was 90 percent accurate until per-field metrics showed tax ids failing.
A contract review UI that fills fields as partial objects stream, then captures corrections as golden cases.
A 100k document month where batching and eval-gated cheaper models cut cost without regressing high-risk fields.
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Lifecycle skills and UI craft skills