Our summary
A startup description can sound plausible without naming who needs it, what problem it addresses or how it earns money. Vincent Forat, founder of Preuve AI, tests whether Jev can flag these gaps. The article examines what the words say, not whether customers will actually buy the proposed product.
Forat asks three yes-or-no questions about each description. Probabilities of at least 0.8 count as yes, and at most 0.2 as no; a keyword check handles the middle range. He reports 326 correct answers among 332 clear decisions, and 349 correct decisions out of 384 for the combined method.
The descriptions are invented, short and clean, and the questions were tuned on the same set. One scenario's expected answer was later changed across all eight languages. The full data is not published, and the feature was not online when the article was written. This commercial case study does not establish accuracy with real users or a reliable ranking of languages.
Key takeaways
- Decide whether a description must state a problem directly or may imply it. Different interpretations change what counts as a correct answer.
- Count the backup's mistakes too. The roughly 98% result excludes 52 uncertain decisions; the full combined method reached 349 out of 384, or 90.9%.
- Test new, untuned descriptions from actual users before relying on the feature. A description naming customers and revenue is not evidence of demand.