Use Cases

There are many ways to classify how Discovery can be deployed and used to add AI and knowledge graphs to search applications. A variety of common business and functional use cases are described here to illustrate the flexibility and usefulness of the platform. 

Business Use Cases 

Intelligent Enterprise Search 

According to a McKinsey report, employees spend 1.8 hours every day – 9.3 hours per week, or 23% of their time, on average – searching and gathering information.  And in today’s economy, with workers increasingly working from home, good corporate information resources have become even more critical.  

But if that is the case, why are corporate intranets (traditional enterprise search) so often maligned? Why do CIOs think they have low return on investment? Why do employees always complain “our search stinks.”  In a chicken-or-egg situation, it is because corporate search applications are so poorly executed.  

But employees rely on internet search every day. The answer to maximizing worker productivity in information search is to deliver a Google-like search experience for corporate intranets. To do this, Google leverages AI and knowledge graphs, and so should your intelligent enterprise search applications. 

E-Commerce Search 

E-Commerce is booming in the post-pandemic economy. And E-Commerce search represents a business use case with indisputable ROI. Research indicates that customers who use search are 2.4 times more likely to buy. They also spend 2.6 times more than other customers. Additionally, 34% of search queries are non-product queries. This could include searches on shipping options, credit options, or return policies. 

The major online retailers may have addressed their customers’ search needs, but the Baymard Institute declares that the state of e-commerce search is “broken”, with only a handful of sites delivering a decent search experience.   

One cause is that e-commerce platforms used by small and medium online retailers do not offer the search functionality necessary to deliver a good search experience. Discovery can complement those platforms to cost-effectively deliver that search experience while your e-commerce platform manages the warehousing and back-office integration aspects of your website. 

Customer Portals 

Whether you are an online retailer, or a company that provides B2B products and services, customer portals play a key role in customer self-service strategies. According to the Service Desk Institute, a good portal can deliver business benefits such as reduced customer support costs, increased customer satisfaction, and the ability to offer round-the-clock support. 

Discovery can help deliver a customer portal experience that helps you realize all these benefits by delivering a search experience that can understand customers’ natural language queries, and deliver the correct answer through direct answers from a knowledge graph, or extractive answers from FAQs, knowledge bases, PDF documentation, and other information sources. 

Content Portals 

Good search is critical for content portals provided by information and media publishers. Whether you are talking about an online subscription to the Bloomberg Finance, a free public portal like the US National archives, or a streaming service like Netflix. In this instance, the content IS the product – and the portal is the customer or user’s access to the content. 

Discovery can be a critical component of a content platform that delivers relevant search results to natural language queries about content. Discovery can also be used to power content recommendation engines, resulting in an optimal experience for content consumers. 

Search & Match 

Search and match applications are special search use cases where the submitted query may be an entire paragraph or document, to search for documents or content in a repository that meet certain matching criteria. Examples include matching resumes to job openings in recruiting; patent searches for potential new patents; similar research in academic repositories; or even molecular formulations in scientific databases. 

In each of these (and similar) use cases, a Discovery-powered search application can automate manual processes or increase the efficiencies of high-salaried knowledge workers. 

Functional Use Cases 

 Question Answering Systems 

In 2022, Google search statistics indicate that more than 21% of queries use 5 or more words – meaning users are likely typing in full questions.  That figure is likely to grow significantly as people become accustomed to asking full natural language questions on search applications. 

Question answering systems are viewed as the future of search. Google and Bing have trained legions of consumers to be able to type full, natural language questions in a search bar, or ask full questions from digital assistants. These online search engines use AI technologies like machine learning and natural language processing, along with knowledge graphs, do deliver the search experience user expect today. 

Discovery is a platform that can integrate and orchestrate the different cloud technologies available today to complement and enhance existing search applications will full Question Answering capabilities.  

Knowledge Management 

Knowledge management (KM) is the process by which an enterprise gathers, organizes, shares and analyzes its knowledge in a way that is easily accessible to employees. This knowledge includes technical resources, frequently asked questions, training documents and people skills. 

KM is a complementary business process that can ensure the success of enterprise search or corporate intranet deployments. Discovery can leverage the taxonomies, vocabularies and content management processes developed in KM to ensure that content is properly ingested, processed and indexed to ensure complete and relevant results in enterprise search applications. 

Document Understanding 

Document Understand helps search applications “know” what documents are about. To answer a search query like “find all construction and renovation contracts in Saudi Arabia,” a search application would have to deconstruct or “understand” the query, and then submit a query to a search engine for the answer.  

The result might come from a search index or knowledge graph; but to populate those databases, platforms like Discovery first have to “read” or deconstruct all relevant documents (contracts) to extract key features from the document and hydrate the databases. This is the rough equivalent of “understanding” the document. 

Discovery leverages advanced cloud-based natural language and machine learning services like Google BERT, Amazon Comprehend, or Azure Cognitive Services to process and “understand” large-scale document repositories to support various document search applications. 

Content Tagging and Processing 

Users can build entire intelligent search applications around Discovery – from content ingestion and processing to index and knowledge graph hydration, to comprehensive UIs. However, many information publishers and independent software / SaaS vendors (ISVs) may already have significant investments in their applications. 

In this case, they can still leverage Discovery to do the important job of content processing and tagging. This is the process by which content metadata is created or enriched so that search indices and knowledge graphs are hydrated with the information needed to improve search results and relevancy in the application. Even this seemingly mundane function is enhanced in Discovery by the incorporation of AI and advanced NLP services. 

Embedded Search Applications 

Sometimes search applications do not take the form of a traditional search bar. This could include highly faceted search applications for travel reservations, GIS-based search applications, or even ride-share applications like Lyft or Uber. In these use cases, search is just one (albeit complicated) feature in a more complex application platform. 

Rather than having to develop an entire search platform from scratch, independent ISVs can leverage today’s API-driven cloud architectures to have just the search portion of their application powered by Discovery on its own cloud infrastructure. This would allow the ISV’s developers and support teams to focus on elements of the application platform that represent the core competencies of their business. Discovery and the search functionality could be managed by a specialized team, or the entire search function could be delivered as a special managed service like Pureinsights’ SearchOps.