The proven deep-learning models that drive Layar and Axon deliver 97% query accuracy. This unprecedented precision provides a powerful and reliable way to reach insights quicker, as well as gain knowledge you might have missed with other approaches.

Type in and track your queries with the ability to toggle between them. Construct questions like speaking with a colleague. View actual snippets of content relevant to your search, not just file names.

Query results are categorized and color-coded for quick assessment. Click on any content piece to see the full, primary source for further evaluation of the evidence. Knowledge graph results are also continuously updated as Layar takes in new content, creating a dynamic experience. 

Results get plotted for a streamlined, visual representation. With the option to overlay plots from multiple queries, quickly spot overlaps and discover novel relationships that may have otherwise gone unnoticed.

Export metadata, literature and answers into GraphML, CSV or JSON for further downstream analysis and easily connect Axon knowledge graphs to third-party applications.

Traditional keyword searches provide users with the most common answer to a question. Vyasa’s natural language question answering understands the context around complex data sets, allowing users to go beyond traditional search results and uncover unique insights in their data, ultimately uncovering needs in the haystack.

Axon runs deep text analysis out of the box, more accurately and effectively than earlier techniques. Your organization doesn’t need to spend months building an ontology (though you can incorporate existing ontologies for a customized analytic boost). You also don’t need to establish rules or a regular expression (regex).

A key part of our deep learning text analytics lies with named-entity recognition, to identify and categorize terms and phrases from content connected by Layar.

In fact, Layar leverages NER to identify and categorize terms and phrases from content already integrated in your data fabric. NER concepts are continuously updated and refined by our deep learning models to reflect the current language of the domain and novel terms not previously mentioned in literature.

Our NER:

• Accounts for grammar nuances and search context to capture relevant content that might not match at first glance — so-called “variants”
• Identifies and retrieves semantically similar terms and related concepts
• Automatically updates as domain language evolves and new terms emerge