What is Jev? How to use it in Sigma through MotherDuck

Jev is a text-classification AI model from TypeSafe AI that returns structured labels, probabilities, or category selections rather than generating prose, positioning it as a lower-cost alternative to general-purpose LLMs for large-scale text analysis. Sigma’s public-beta AI columns can use Jev through MotherDuck’s `prompt_jev()` function to classify workbook data row by row, enabling users to filter and analyze results as standard data columns. In a reported test, tagging 6,578 customer reviews by referenced product features took 4.6 seconds and cost 5.3 cents, while projected comparisons suggest substantially lower costs than Claude Haiku for similar workloads. The approach is intended to let business users experiment with classifications without extensive data-engineering work, while Sigma provides caching, access controls, and monthly token caps. However, Jev cannot summarize or infer context absent from an individual record, its accuracy should be validated against trusted labels, and text submitted through MotherDuck is sent to TypeSafe for processing, requiring security review for sensitive data.

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