Built by Metorial, the integration platform for agentic AI.
Removes JavaScript, CSS tags, JSON, and other markup from text, returning pure decoded plaintext. Useful for preprocessing HTML or markup-heavy content before analysis.
Translates text between supported languages. Supports auto-detection of the source language. If source and target languages are the same, paraphrasing is applied instead. Handles obfuscated and slang content, preserving emotional register — profanities and slurs are translated faithfully.
Detects the languages used in a text fragment and returns a breakdown by offsets. Useful for identifying the language(s) of unknown text or multilingual content where different parts may be in different languages.
Retrieves all languages supported by Tisane, with metadata including native name, English name, script direction, and encoding. Use this to discover valid language codes for other tools.
Looks up a word in Tisane's language models to retrieve its senses (meanings), definitions, and crosslingual family IDs. Can also retrieve inflected forms, hypernyms (broader terms), and hyponyms (narrower terms) for a given family. Useful for linguistic research, understanding word senses, and exploring semantic relationships.
Calculates the semantic similarity between two text fragments, returning a score between 0 (completely different) and 1 (identical meaning). Supports cross-language comparison, allowing you to compare texts written in different languages.
Compares two compound named entities and identifies whether they refer to the same entity, with specific differences if they don't match. Supports cross-language comparison. Currently supports person entities only. Useful for name matching, deduplication, and identity resolution across different languages and naming conventions.
Performs comprehensive NLP analysis on text including abusive content detection (hate speech, cyberbullying, sexual harassment, criminal activity), sentiment analysis, named entity extraction, and topic detection. Supports 30+ languages. Configure the analysis through optional settings to enable/disable specific features like document-level sentiment, word-level tokenization, parse trees, and explanations.