Tag: Document Insight Extraction


More than 90% of clinical-trial compounds fail to demonstrate sufficient efficacy and safety. To mitigate this issue, many look to third-parties to conduct extensive research via systematic literature review (SLR) to gather the relevant information needed to validate the investment and approach to a trial. While providing valuable insight, each SLR can cost over $140,000.


SLRs are largely conducted via manual processes – with researchers needing to compile, review and analyze large sets of unstructured content. As a result, these processes are incredibly time consuming due to the amount of data and grey literature that needs to be consumed and reported on to make SLRs successful. Due to the nature of this work, insights can be missed and timelines can be delayed which hold up productivity for the end customer.


Advancements in deep learning text analytics are enhancing systematic literature reviews by improving the accessibility and searchability of unstructured content. Vyasa has developed high-performing deep learning models that understand context and can identify key terms that improve the accuracy and time spent on systematic literature reviews. We then take this a step further with our Layar data fabric, a novel data architecture that unifies content sources into a single platform. With the Vyasa platform, users can:

  • Integrate siloed data sets into a single, searchable platform.
  • Search unstructured content in natural language.
  • Explore their data via highly-visual knowledge graphs, dashboards and smart spreadsheets.
  • Export unstructured content into structured formats.

By leveraging Vyasa, users can improve query accuracy by 97% while reducing research times by as much as 90%, leading to smarter, more efficient systematic literature reviews.


Call center representatives are under immense pressure. They must process large amounts of information and quickly answer questions all while managing caller expectations. In most cases, they’re armed with outdated technology and complicated software which doesn’t make their work easier. No wonder call centers have such a high turnover rate.


To effectively respond to caller requests, representatives typically rely on searching lengthy documents that are in a variety of formats and saved across various locations. Finding the right insights they need is time consuming and in most cases they’re only collecting a small portion of the information available to them. This leads to delays, missed or incorrect information and ultimately, a poor customer experience.


Advancements in data management and deep learning can improve the way call center representatives gather information and serve customers. Through a new data architecture known as the data fabric, Vyasa can unify all data sources available to a call center into a single platform. Deep learning models built by Vyasa can then be applied to the unified data making it easy to search and access in a matter of seconds.

With Vyasa’s Layar data fabric and deep learning applications, call centers can:

  • Make data sources accessible across departments. (No need to duplicate your data or storage and no data lake required.)
  • Quickly search for and access documents in a single location.
  • Query large sets of documents via smart spreadsheets and dynamic knowledge graphs.
  • Discover related concepts and insights with named entity recognition.
  • Extract information into easily shareable file types.

With Vyasa, call center representatives can improve research accuracy by 97% while decreasing analysis time by 90% leading to enhanced call center response and a more positive customer experience.