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Classify the emotion(s) a text expresses from a closed taxonomy (joy, anger, sadness, fear, surprise, disgust, neutral) with a primary emotion, secondary emotions, and intensity.

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Classify a user message against a runtime intent taxonomy (label + description) for NLU and chatbots, with confidence and an explicit out-of-scope fallback flag.

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Per-aspect sentiment analysis — identify each feature or topic a text opines on and give it its own sentiment, backed by a verbatim quote, plus an overall document sentiment.

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Grade whether a summary is grounded in its source document (0–1) with each unsupported claim itemized — usable directly as an LLM judge in evals and as a runtime hallucination gate.

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Generate a headline plus alternatives with a style knob (neutral / engaging / seo) and a hard word limit — every claim grounded in the text, no invented specifics.

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Extract up to N key points from a document, each backed by a verbatim supporting quote — groundedness you can verify with a substring check, no judge needed.

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Summarize an email thread into a brief with a closed status enum (resolved / waiting_reply / action_needed / informational), who the ball is with, and stated next steps.

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Turn a meeting transcript into structured minutes — summary, decisions, action items, open questions — with owners and due dates only when explicitly stated, never inferred.

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Summarize a document with format (paragraph/bullets) and length (one sentence/short/detailed) knobs — grounded strictly in the input, with key topic tags.

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Detect the language(s) of a text from a closed set of 41 ISO 639-1 codes — handles short snippets, code-mixed, and transliterated text, with per-language confidence.

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Grade a source/translation pair on independent accuracy and fluency axes (0–1) with an itemized, severity-tagged issue list — usable directly as an LLM judge in evals.

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Translate text with an explicit register knob — formal, informal, marketing, or technical (Sie/du, keigo, copywriter idiom) — plus notes on the tone choices made.

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