Loop AI Labs: Artificial Intelligence to Interpret Unstructured Data

With the enormous growth of unstructured data like e-mails, customer requests or transcriptions, enterprises often lack the tools to analyze and extract the relevant value from the information. Though common structuring techniques such as manual tagging with metadata or text mining are being heavily used, companies are still faced with numerous difficulties while extracting information, particularly when the text data is in more than one language. “Bridging the gap between unstructured data and insights, we offer services that model organizations’ data and build an Application Programming Interface (API), which enables them to understand and exploit the unstructured text in any language,” begins Bart Peintner, CTO, Loop AI Labs. Founded in 2012, the company offers the Loop Cognitive Computing Platform, a machine intelligence technology that automatically interprets natural language and extracts structured representations of the concepts and relationships contained in small or large amount of texts. The company’s APIs can be deployed to work on the cloud or using a physical appliance on the site of the company’s own data center.

Any kind of text data can be injected in the APIs, and a structured representation can be extracted from it automatically

Developers can organize any kind of unstructured text by integrating the model produced by the Loop Cognitive Computing Platform into their applications, using a collection of simple APIs. Using these APIs, Loop helps developers leverage any kind of unstructured text without data science or artificial intelligence experience, making concept extraction a process of simple API calls. The Person Digital GenomeAPI enables applications to instantly derive a deep profile of a user, including interests, life context, and personality insights. The deep profile helps businesses understand their clients' preferences and create the next generation of personalized applications.“The text data can be built in the form of a model to understand the conceptual content, even from the smallest text data