Cognitive QnA: Performing Cognitive Search on an Unstructured Dataset

1. Jon recently joined a leading bank as a contact center executive 
2. Although he completed his training, he often struggles to find the correct answers to customer queries 
3. The bank continuously keeps adding new products or services and modifying regulations for existing ones. Jon needs to keep track of all this 
4. Jon has access to a knowledge base that has all the information Jon needs, but he needs to sift through various documents manually to look for answers 

Pretty tough job for Jon. Isn't it? Well, most contact center agents face a similar challenge. Result? Low agent productivity, higher job dissatisfaction, and poor service to customers.  

However, there's a way out of Jon's problem - adding Cognitive Search capabilities to the knowledge base and enabling Cognitive QnA.  

What is Cognitive Search?

Cognitive Search is an advanced search technique that leverages AI (Artificial Intelligence) to enhance the search experience. It has powerful features that previous search techniques lacked. For starters, it can perform searches over unstructured, diverse data sets - pdfs, Word documents, knowledgebase, website pages, and even images (if the search is advanced enough to incorporate image processing). Also, it continuously learns from past search results. 

Cognitive Search is the next generation of Enterprise Search. It enhances how employees or customers discover and access relevant information from multiple, diverse data sets - one of the main reasons why businesses are shifting from Enterprise Search to Cognitive Search. 

Technologies Involved in Cognitive QnA

Apart from regular search technologies, Cognitive Search uses some advanced technologies, such as -  

Semantic Search:

Instead of searching data for corresponding keywords, Semantic Search focuses on the intent and contextual meaning of the search query.  

For example, your query is "Cognitive Search Meaning."  And the next query is "How does it work?" Now, the Semantic Search would understand that ''it" of the second query refers to   "Cognitive Search." 

NLP (Natural Language Processing) and search history help Cognitive Search understand the semantics of a user query. 

Fuzzy Matching:

Fuzzy matching is a technique that helps find matches less than 100% accurate. For example, when you search 'Appl,' 'Aple,' or 'Appl,' the fuzzy matching algorithm provides matches corresponding to 'Apple.' 

Use Cases of Cognitive QnA

AI Agent Assist

Cognitive Search plays a vital role in AI Agent Assist - virtual assistants that help human agents find answers to customer queries when the agents are talking to customers. It helps human agents resolve customer queries faster, increasing customer satisfaction (CSAT) scores and reducing MTTR (mean time to resolve). 

Customer Support

You can deploy conversational AI Chatbots and Voicebots built on Cognitive Search to enable Cognitive Q&A, facilitating customers to self-serve. This way, customers can discover and access information related to your products and services and find solutions to their issues. 

Onboarding

When a new employee joins your organization, the first few days can be daunting for them. You can provide them with Cognitive Search, which has cross-platform search capabilities, to make the transition easier for them. 

Summary

Artificial Intelligence is continuously widening its scope and transforming more and more technologies with time - Cognitive Q&A is one such technology.  

Businesses across the world have started leveraging Cognitive Search for various use cases, from customer support to employee onboarding.  

If you have any questions or need assistance related to Cognitive QnA, feel free to reach us at connect@floatbot.ai