We can help you bridge the gap between human language and computers through text analytics. At its core, text analytics is breaking a stream of text into meaningful words or phrases, but meaningful is a relative term. Our deep knowledge of content will help you find exactly the information inside of your content that is important and meaningful.
In addition, we will help you discover how you can use text and data together to identify and solve your business problems. We can provide an overview of the technologies behind analyzing text and practical applications of the field and sift through the rapidly-changing language surrounding text analytics. We help organizations understand and navigate the growing list of technologies that can help wrangle enormous loads of text into usable information, including machine learning, natural language processing, deep learning, neural networks, taxonomies, ontologies, RDF, triples.
We are experts at building taxonomies and ontologies that organize, increase findability of, and enhance search of enterprise content. Those taxonomies and ontologies are often used to auto-classify content and help manage metadata that drive text analytics and quality search results. We also have experience creating triples for triple stores such as MarkLogic, and we understand RDF, RDFa and SKOS.
Content strategy ensures that visitors find the information on a website useful and relevant and creates a meaningful interactive experience. We create content strategy plans that take a content-first approach that lays the groundwork for the creation, publication, and governance of relevant, relatable, and usable content. They also set the tone for the site and related content.
Information architecture is essential for successful knowledge and information management within organizations. It starts with understanding the organization – its needs, individuals, technology capabilities and business goals. It proceeds with careful planning and succeeds with thoughtful understanding of content.