It’s a problem that has plagued organizations for decades: How to extract business value from the massive amount of content they have distributed across departments, regions, and systems — buried, disorganized, and lost despite the many knowledge management initiatives that are highly funded but have yielded low returns. 

The cost of not solving this ubiquitous problem? Customers, partners, and employees lose thousands of hours of productivity trying to find answers they need to drive critical workflows and business results. 

Consumer tech companies generate masses of product and technical documentation; pharmaceutical companies create patent applications, reams of Federal regulatory documentation, research, and information on side-effects and labeling. Online retailers, hardware manufacturers, software-as-a-service (SaaS) companies…all have product, service, troubleshooting, educational and installation instructions, as well as volumes of internal policy and process documentation, stored in many different repositories to support a variety of internal and customer needs.

Consider just one example of an internal helpdesk that serves employees with IT support. The trends point to an increasingly complex IT environment:

  • The average medium-sized company uses up to 364 SaaS tools, all of which require internal IT support
  • 90% of the data being generated today is unstructured, which automated systems like chatbots struggle to process
  • A single employee can spend up to 5 hours per week in search of internal documents, driving productivity and efficiency levels down.

The challenge then becomes, How do you get content from those repositories into the hands (or the systems) where the information is needed most? 

Most of these organizations have turned to chatbots, intelligent virtual assistants (IVAs), and interactive voice response (IVR) solutions to scale and automate the delivery of this critical information. It doesn’t take long, however, to discover that in order to apply these “modern” technologies, organizations are forced to recreate, duplicate, or refactor the content into manually programmed questions and answers. 

The end result? Duplication of effort, an error-prone, low-accuracy self-service system that is regularly out of date and leaves many questions or support tickets resolved.

This realization has made the unification, automation and digitization of enterprise knowledge an increasing priority. This growth has created significant challenges, such as knowledge silos, inefficiencies, and general barriers to decision making and customer and employee service.

By closing the gap between knowledge and its consumers, a Knowledge Operating System can accelerate digital transformation and automation initiatives across businesses.

The dawn of the Knowledge Operating System

Innovative companies are now addressing this by implementing a Knowledge Operating System, virtually unifying their knowledge sources into a single AI-enabled environment that augments enterprise-wide productivity. 

A Knowledge Operating System is an enterprise architecture that integrates siloed knowledge sources and transforms the content into a digital, answer-ready format to meet a variety of business-critical demands. 

Used to fuel and optimize question-and-answer systems for internal and external customer support, a Knowledge Operating System uses artificial intelligence (AI) and natural language processing (NLP) to provide higher-quality, more accurate results than commodity chatbots, IVAs, and IVR solutions. 

The most advanced Knowledge Operating System is one that:  

  • Allows the content to stay where it resides — in its systems of record such as SharePoint,, Confluence, ServiceNow, Zendesk, etc. 
  • Delivers answers to any destination via advanced API integrations — directly to chatbots or IVAs, collaboration tools such as Slack or Microsoft Teams, or internal web sites or wikis. 
  • Includes features that improve the source content over time, through user validation of accurate answers and automated analytics that alert administrators to missing information that can then quickly be added. 
  • Offers advanced technologies for “reading” even the most complex technical documents and transforming the content into answers. 
  • Can be implemented within a week, unlike other AI systems that can take months.
  • Drives quantifiable business benefits such as doubling or tripling the accuracy of chatbot or IVR accuracy. 

Simply put, a Knowledge Operating System allows for more holistic, knowledge-centric decision making.

Historically, an enterprise may have had different content and knowledge platforms aligned to specific lines of business. For example, you might have HR policy and benefit information, product knowledge bases, field safety policies and procedures, cybersecurity policies and procedures, regulatory information — all housed in different and separate environments.

However, a Knowledge Operating System can allow users and decision-makers to access the organization’s knowledge more cohesively in order to better service employees, customers and partners. By closing the gap between knowledge and its consumers, a Knowledge Operating System can accelerate digital transformation and automation initiatives across businesses.

The Knowledge Operating System aims to create more fluidity across environments and reduce the problem of “data gravity” — the idea that data (or, in this case, enterprise content or knowledge) becomes more difficult to move as it grows in size. A Knowledge Operating System makes all knowledge available across the enterprise for a variety of business applications.

Businesses applying this type of knowledge framework can expect benefits from a more disciplined approach to managing one of the organization’s most valuable, yet startlingly inaccessible, assets.

Activate your enterprise knowledge

By connecting directly to knowledge sources from their existing location, Pryon creates an integrated knowledge collection and transforms the content into a digital, answer-ready format to meet a variety of business-critical demands.

Pryon: How a Knowledge Operating System enables Knowledge-as-a-Service

Pryon is the first full-stack AI platform that allows the point-and-click creation of collections of enterprise content, which are enriched with AI techniques and optimized for question and answering, to create the Knowledge Operating System. The KOS then enables the delivery of Pryon’s Knowledge-as-a-Service, which organizations can activate to empower both internal and external customers who need accurate, immediate access to answers.   

Pryon’s proprietary engines and models ingest existing application data and documents via an automated pipeline, enabling a Siri-like experience inside or outside the organization. Through its KaaS platform, Pryon delivers the highest accuracy answers to customers, partners, and employees in milliseconds, the first time asked.

The Knowledge Operating System is composed of multiple specialized layers designed to access, optimize, and deliver accurate answers to the users and systems that need them:

Knowledge collection layer: This is the “virtually unified” representation of an organization’s internal knowledge. 

Ingestion layer: The component that allows for native connection to knowledge sources, without requiring users to move, refactor, or duplicate the content.  

Transformation layer: The transformation layer refines the content into a cleansed and vectorized state to ensure that only relevant information is processed for answers (e.g. information from extraneous document parts such as a table of contents, page title, or footnote is ignored).

Knowledge analysis & improvement layer: These features allow administrators to monitor critical usage analytics and identify trends in knowledge demand, discover and fill gaps in required information, and fine-tune quality control measures.

Case study: Fortune 500 manufacturing company estimates $4.4 million in savings

Faced with underperforming chatbots that were threatening to compromise employee experience and productivity, a large tech manufacturer turned to Pryon to evolve its IT support strategy. (Read the full case study here.)

Now, Pryon transforms more than 500 documents into answers to more than 20,000 questions per month with 90 percent accuracy — three times the accuracy of its previous chatbot — and estimates $4.4 million in savings from support ticket reduction.

Pryon’s no-code AI Knowledge Operating System makes it easy to scale and automate exceptional employee support while allowing the workforce to focus on strategies and innovations that keep the company competitive.

In an additional pilot for the customer support organization, Pryon’s expert solution engineers were able to leverage the Pryon Knowledge Operating System to quickly ingest the relevant internal documentation from its existing locations, create knowledge collections organized by product and service categories, and define user access levels.

The first wave of question-and-answer testing showed a 3x increase in accuracy compared to tests without Pryon. This means that three times more answers were accurate, and pointed users to the precise point in the documentation where the answer was found.

In addition to this manufacturing company’s use of a Knowledge Operating System to provide IT and customer support, other use cases for KOS and Knowledge-as-a-Service include: 

  • Delivering safety policies and procedures to field workers in utility and other service industries
  • Scaling rapid access to cybersecurity awareness and remediation practices 
  • Enabling employee self-service to HR policy and benefit information
  • Streamlining access to clinical and biomedical research
  • Organizing and accelerating access to volumes of legal discovery and precedent documents.