.svg){kind=icon}
A world-class NLU Engine to extract accurate entities from Arabic texts.
Our Arabic NER engine uses advanced machine learning techniques to extract entities across a wide variety of textual documents and classifies them into more than 20+ predetermined categories (Names, Events, Organizations, and many more).
Discover in-depth insights and emotions from arabic texts.
The sentiment analysis engine understands the intent and contextual meaning of the texts and generates accurate sentiment results.
An engine used to lookup for answers from documents and text in real-time.
The question-answering engine navigates through the data and converts unstructured information to meaningful structured information by extracting and retrieving the answers to the queries utilizing machine learning NLP techniques.
An engine that summarizes textual documents and highlights key information.
Leveraging advanced machine learning NLP techniques, the summarization engine generates summaries of the key information central to the textual Arabic documents by analyzing the texts and extracting word-to-word sentences that are scored in their order of importance.
An Engine that Categorizes textual data into different classes based on a range of topics.
Using advanced machine learning techniques, our Classification engine maps textual information to different categories. It accomplishes this via a trained NLU model with pre-classified examples of text belonging to various classes.
An engine that matches various ways in writing or pronouncing names in Latin or Arabic for matching and checking.
Engine converts text whether it is latin or Arabic and considers phonetics, character positions and machine learning techniques to map and score matching of names.
