Licensed counselors (LPC or LMHC) to build the counseling scenarios frontier AI labs use to test model judgment and safety. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
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Licensed veterinarians to author and grade the clinical scenarios frontier AI labs use to test veterinary reasoning. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Licensed dentists to author and grade the clinical dentistry scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Certified pharmacy technicians to build the medication workflow scenarios frontier AI labs use to test practical accuracy. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Ukrainian gives AI structure it cannot fake: cases, verb aspect, and agreement that must hold across the sentence, plus the ty and vy distinction. The model also slips Russian-influenced phrasing into Ukrainian, which a native reader spots at once.
Dutch catches AI on word order and on the je versus u choice that sets the tone of a sentence. It builds compounds and diminutives clumsily and rarely notices when Flemish usage would read differently from the Netherlands, both of which a native speaker feels at once.
Malay trips AI on register: formal written Malay and everyday colloquial speech pull in different directions, and the model blends them awkwardly.
Hebrew exposes AI through grammatical gender: verbs and adjectives agree with the person addressed, and the model routinely gets it wrong. It also blurs modern usage with biblical register and struggles with the root patterns a native writer navigates by instinct.
Tamil catches AI on its split between everyday spoken forms and formal literary Tamil: the model mixes the two, producing text no one would say aloud or write in earnest.
Polish gives AI a lot to break: seven cases, verb aspect, and agreement that must stay consistent across the sentence. It also fumbles the formal pan and pani and produces wording that parses but sounds unmistakably translated, which is where a native speaker steps in.
Bengali catches AI on the you it chooses: tui, tumi, and apni each carry a different closeness, and the model picks wrong. Its script handling can wander, and phrasing from Bangladesh and West Bengal is not interchangeable, though it often treats them as if they were.
Tagalog trips AI on the verb focus system and on the po and opo markers that carry respect. It handles the natural mix of Tagalog and English awkwardly and reaches for stiff, formal phrasing where real conversation code-switches freely, which a native speaker catches at once.
Russian trips AI on structure it cannot fake: six cases, verb aspect, and agreement that has to hold across a whole sentence.
AI produces textbook Italian that still feels off: it forces gender and number agreement mechanically, misses the formal Lei, and leans on wording that sounds translated rather than said.
German gives AI plenty to get wrong: it botches case endings, builds compound nouns no one would actually say, and misses when du should be Sie. Fluent-looking output can still feel mechanical, and only a native ear catches when Austrian or Swiss usage would read differently.
AI writes fluent-looking French that still slips on what natives feel automatically: it mishandles tu and vous, forces gender agreement in odd places, and reaches for wording that sounds translated rather than spoken.
Automated metrics can't judge whether Turkish text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Automated metrics can't judge whether Indonesian text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Automated metrics can't judge whether Arabic text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Automated metrics can't judge whether Portuguese text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Automated metrics can't judge whether Hindi text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Automated metrics can't judge whether Spanish text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.
Korean punishes the mistakes AI loves to make: it mixes speech levels, sliding between formal and casual within a single exchange, and drops or misplaces the particles that carry meaning. The honorifics a native speaker chooses without thinking are exactly where the model slips.
Vietnamese is unforgiving of the small errors AI makes constantly: it drops or misplaces the tone diacritics that change a word entirely, and it stumbles over the pronoun system, where the right term depends on age and relationship.