Claire.ai derives insights from unstructured text.
Claire.ai extracts 40 pre-defined entities which includes attributes such as names (non-region specific), location-related information, monetary values etc along with other custom-defined entities. These are built around in-demand use-cases through SDKs and cloud-deployable models. Claire.ai’s NLP library also extends support for parsing unstructured content, text summarization, intention recognition, text classification and conversational interfaces with unforgettable user experiences.
Understanding the context in content.
The biggest problem in language processing is to build machines that read the context from content. Claire.ai aims to abridge the intelligence gap between written text and contextual understanding. Claire.ai’s architecture is modular by design allowing businesses to process a diverse set of document types across multiple languages. Claire.ai does the work of extracting information, identifying key insights, performing analysis of the text at scale, and integrates outputs to any communication platform (Whatsapp, Slack, Email etc.)
In the process of language understanding, Claire.ai algorithmically captures words that have reserved definitions in the context of intercultural communication preserving their meaning. This has enabled us to build multiple use-cases like understanding gender sensitivity, political and religious inclination in conversations.
Most of the data available on the internet is unstructured. Data comes in all shapes and sizes - in tables, webpages, audio, text, video, etc. Claire.ai combines its extraction API to fetch unstructured data from any source to a structured machine-readable format - rending information useful for storage and analytics. Extraction is automated - eliminating the need to enter data by hand.
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